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RandomForestClassifier | C2 | Used Cars Price-Range Prediction | Per the model, class C1 has a prediction probability of 10.50 percent, whereas class C2 has a predicted probability of 89.50 percent. As a result of the model, it can be determined that C2 is the most likely label for the given scenario. All of the input features are shown to contribute to the above conclusion, with F5, F4, and F2 having the most influence on the classification decision. The least influential features with regard to this classification are F1, F3, F9, and F6, whereas, the impact of F10, F8, and F7 can be classified as modest. The large positive contributions of F4 and F5 are responsible for the model's high confidence which further supported by the positive contributions of F10, F1, and F3. In conclusion, the negative features F2, F8, F9, F7, and F6 favour labelling the case as C1 hence the associated predicted probability. | [
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"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F7, F1 and F9) with moderate impact on the prediction made for this test case."
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] | {'F4': 'Power', 'F5': 'car_age', 'F2': 'Transmission', 'F10': 'Fuel_Type', 'F8': 'Name', 'F7': 'Mileage', 'F1': 'Engine', 'F9': 'Owner_Type', 'F3': 'Kilometers_Driven', 'F6': 'Seats'} | {'F4': 'F4', 'F5': 'F5', 'F8': 'F2', 'F7': 'F10', 'F6': 'F8', 'F2': 'F7', 'F3': 'F1', 'F9': 'F9', 'F1': 'F3', 'F10': 'F6'} | {'C2': 'C1', 'C1': 'C2'} | High | {'C1': 'Low', 'C2': 'High'} |
AdaBoostClassifier | C2 | Basketball Players Career Length Prediction | The classifier says that C2 is the most likely label for the provided data with relatively high confidence. It is crucial to remember, however, that there is a 21.80% possibility that it is C1. F10 and F1 are the major driving variables for the aforementioned classification or prediction choice. The remaining variables F6, F11, F12, and F9 have a modest to minor impact on the selection made above. Among the input variables, F6, F9, F18, F14, and F3 are the subset that have a negative influence or contribution whereas all of the remaining variables have a positive impact. In essence, the substantial positive contributions of F10 and F1, together with the contributions of additional positive variables such as F11, F12, F2, and F4, account for the classifier's confidence in this classification. | [
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"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F2, F4 and F18?"
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] | {'F10': 'GamesPlayed', 'F1': 'PointsPerGame', 'F6': 'Steals', 'F11': 'MinutesPlayed', 'F12': 'DefensiveRebounds', 'F9': 'Rebounds', 'F2': 'Blocks', 'F4': 'FreeThrowAttempt', 'F18': 'FieldGoalPercent', 'F13': 'FreeThrowMade', 'F14': 'OffensiveRebounds', 'F19': 'FieldGoalsMade', 'F8': '3PointAttempt', 'F5': 'FreeThrowPercent', 'F16': '3PointMade', 'F7': 'FieldGoalsAttempt', 'F15': 'Turnovers', 'F3': 'Assists', 'F17': '3PointPercent'} | {'F1': 'F10', 'F3': 'F1', 'F17': 'F6', 'F2': 'F11', 'F14': 'F12', 'F15': 'F9', 'F18': 'F2', 'F11': 'F4', 'F6': 'F18', 'F10': 'F13', 'F13': 'F14', 'F4': 'F19', 'F8': 'F8', 'F12': 'F5', 'F7': 'F16', 'F5': 'F7', 'F19': 'F15', 'F16': 'F3', 'F9': 'F17'} | {'C2': 'C2', 'C1': 'C1'} | More than 5 | {'C2': 'More than 5', 'C1': 'Less than 5'} |
RandomForestClassifier | C1 | Printer Sales | C1 has an 83.0% chance of being the correct label for the case under consideration, making C2 the least likely class with a predicted likelihood of 17.0%. F16, F1, and F25 features have a significant impact on class selection here while on the other hand, the remaining features are shown to have marginal to no contribution to the classification verdict here. In actual fact, the values for F6, F2, F14, F3, F21, and F18 may have been ignored by the classifier because their respective influences are almost zero. Of the important features, only F13, F23, F11, F15, F22, and F5 are negative and this is mainly because their contribution to selection tends to reduce the chance that C1 is the correct label, preferring that the case is classified as C2. The remaining features such as F16, F1, F25, F12, F10, and F24 strongly contribute positively, increasing the chances of C1 which explains the level of certainty associated with C1. | [
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"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F13, F7 and F8?"
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] | {'F16': 'X8', 'F1': 'X24', 'F25': 'X1', 'F10': 'X2', 'F12': 'X10', 'F24': 'X15', 'F13': 'X25', 'F7': 'X23', 'F8': 'X18', 'F23': 'X4', 'F11': 'X7', 'F19': 'X17', 'F26': 'X3', 'F4': 'X22', 'F15': 'X5', 'F22': 'X9', 'F5': 'X12', 'F20': 'X19', 'F17': 'X11', 'F9': 'X16', 'F6': 'X14', 'F2': 'X21', 'F14': 'X20', 'F3': 'X13', 'F21': 'X6', 'F18': 'X26'} | {'F8': 'F16', 'F24': 'F1', 'F1': 'F25', 'F2': 'F10', 'F10': 'F12', 'F15': 'F24', 'F25': 'F13', 'F23': 'F7', 'F18': 'F8', 'F4': 'F23', 'F7': 'F11', 'F17': 'F19', 'F3': 'F26', 'F22': 'F4', 'F5': 'F15', 'F9': 'F22', 'F12': 'F5', 'F19': 'F20', 'F11': 'F17', 'F16': 'F9', 'F14': 'F6', 'F21': 'F2', 'F20': 'F14', 'F13': 'F3', 'F6': 'F21', 'F26': 'F18'} | {'C1': 'C1', 'C2': 'C2'} | Less | {'C1': 'Less', 'C2': 'More'} |
LogisticRegression | C2 | Music Concert Attendance | C2 is the label picked by the algorithm with about 82.06% certainty, since the prediction likelihood of C1 is only 17.94%. F12, F14, F3, and F18 all contribute significantly to the above classification output and among them, the features that support the most positive contribution to the C2 prediction are F18, F12, and F14, while F3 drives the final prediction against assigning C2 in support of C1. F13 also contributes positively to the classification here, but F11 contributes negatively and like F3 favours C1. Finally, according to the analysis, F7, F9, F6, and F16 all have little effect on the final prediction made by the algorithm for this case. | [
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"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F14, F11 and F13) on the model’s prediction of C2.",
"Summarize the set of features has little to no impact on the prediction?"
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"F17",
"F4",
"F2",
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] | {'F18': 'X11', 'F12': 'X1', 'F3': 'X13', 'F14': 'X3', 'F11': 'X8', 'F13': 'X6', 'F20': 'X2', 'F15': 'X9', 'F8': 'X17', 'F1': 'X10', 'F5': 'X4', 'F19': 'X14', 'F10': 'X20', 'F17': 'X18', 'F4': 'X19', 'F2': 'X7', 'F6': 'X12', 'F7': 'X15', 'F16': 'X16', 'F9': 'X5'} | {'F11': 'F18', 'F1': 'F12', 'F13': 'F3', 'F3': 'F14', 'F8': 'F11', 'F6': 'F13', 'F2': 'F20', 'F9': 'F15', 'F17': 'F8', 'F10': 'F1', 'F4': 'F5', 'F14': 'F19', 'F20': 'F10', 'F18': 'F17', 'F19': 'F4', 'F7': 'F2', 'F12': 'F6', 'F15': 'F7', 'F16': 'F16', 'F5': 'F9'} | {'C1': 'C1', 'C2': 'C2'} | > 10k | {'C1': '< 10k', 'C2': '> 10k'} |
LogisticRegression | C1 | Flight Price-Range Classification | The model is confident in its prediction, as it predicted class C1 with a likelihood of 90.48% and hence, for the given case, there is a smaller chance of it being any other class label. F1 and F12 are deemed the most important features whereas on the other hand all the other features have moderate to minimal amounts of influence. Both F1 and F12 have the same direction of impact, increasing the odds of the predicted label, C1. While F3 and F9 are both encouraging the model to make a prediction of C1, the others F11, F8, and F7 is pushing the model towards a different label. Many features have moderately low impact on the final prediction, but the features F6, F7, and F5 are those with the smallest influence. | [
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] | 89 | 2,333 | {'C1': '90.48%', 'C3': '9.51%', 'C2': '0.01%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F1 (equal to V4) and F12 (equal to V3).",
"Summarize the direction of influence of the features (F3 (equal to V2), F9, F11 (when it is equal to V0) and F10) with moderate impact on the prediction made for this test case.",
"Provide a statement on the features with the least impact on the prediction made for this test case."
] | [
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"F11",
"F10",
"F2",
"F8",
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] | {'F1': 'Total_Stops', 'F12': 'Airline', 'F3': 'Destination', 'F9': 'Arrival_hour', 'F11': 'Source', 'F10': 'Duration_hours', 'F2': 'Dep_hour', 'F8': 'Dep_minute', 'F4': 'Arrival_minute', 'F6': 'Journey_month', 'F7': 'Journey_day', 'F5': 'Duration_mins'} | {'F12': 'F1', 'F9': 'F12', 'F11': 'F3', 'F5': 'F9', 'F10': 'F11', 'F7': 'F10', 'F3': 'F2', 'F4': 'F8', 'F6': 'F4', 'F2': 'F6', 'F1': 'F7', 'F8': 'F5'} | {'C1': 'C1', 'C3': 'C3', 'C2': 'C2'} | Low | {'C1': 'Low', 'C3': 'Moderate', 'C2': 'High'} |
DecisionTreeClassifier | C1 | Airline Passenger Satisfaction | Based on the probability distribution across the classes, the classifier is shown to have a moderately high confidence level in the C1 label assignment, with its likelihood equal to 65.0%, whereas that of C2 is only 35.0%. The prediction decision above is predominantly due to the influence of the variables F10, F9, F1, and F25. On the lower end are the least relevant variables, F21, F22, F20, F18, F19, and F14, with little to no influence on the classifier when assigning a label to the given instance. On the one hand, the top positive variables are F10, F9, and F1, increasing the probability that C1 is the correct label. Also, the top negative variables are F25, F17, F26, and F23, decreasing the classifier's response and consequently shifting the prediction verdict in the opposite direction towards C2. Other variables with a positive direction of influence are F24, F3, F6, F13, F11, F12, F2, F5, and F15. | [
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"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F13 (equal to V2), F9 (equal to V1), F19 (with a value equal to V0) and F26 (value equal to V3)) on the model’s prediction of C1.",
"Summarize the set of features has little to no impact on the prediction?"
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KNeighborsClassifier | C1 | Company Bankruptcy Prediction | The model's output labelling judgement for the case under consideration is as follows: C2 cannot be the label for the given case; C1 is the most likely class label with a 100.0% confidence level. The key driving factors resulting in the aforementioned classification are the values of the input features: F47, F68, F33, F20, F86, F85, and F87. F78, F26, F16, F66, F34, F19, F76, F79, F82, F24, F50, F81, and F55 are the features that have a modest effect on the decision. Aside from the aforementioned input features, all others, such as F71, F12, F83, and F11, are revealed to be irrelevant to the conclusion reached here. Not all of the influential features support labelling the current instance as C1, and they are referred to as negative features. F87, F26, F50, F81, and F55 are the negative attributes that diminish the likelihood that C1 is the correct label in this case. F47, F68, F33, and F20 are important positive features that strongly increase the likelihood that C1 is the correct label. | [
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] | 423 | 2,648 | {'C1': '100.00%', 'C2': '0.00%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F86, F87 and F85) with moderate impact on the prediction made for this test case."
] | [
"F47",
"F68",
"F33",
"F20",
"F86",
"F87",
"F85",
"F78",
"F26",
"F16",
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"F66",
"F19",
"F76",
"F79",
"F82",
"F24",
"F50",
"F81",
"F55",
"F71",
"F12",
"F83",
"F11",
"F60",
"F15",
"F77",
"F67",
"F35",
"F89",
"F45",
"F57",
"F43",
"F52",
"F90",
"F61",
"F62",
"F65",
"F44",
"F64",
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"F10",
"F6",
"F70",
"F38",
"F18",
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"F14",
"F42",
"F1",
"F17",
"F88",
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"F39",
"F69",
"F58",
"F23",
"F56",
"F49",
"F84",
"F72",
"F73",
"F92",
"F48",
"F37",
"F21",
"F8",
"F74",
"F28",
"F13",
"F25",
"F2",
"F36",
"F31",
"F53",
"F80",
"F46",
"F30",
"F59",
"F7",
"F9",
"F93",
"F40",
"F27",
"F75",
"F51",
"F3",
"F91",
"F29",
"F32"
] | {'F47': ' Interest Coverage Ratio (Interest expense to EBIT)', 'F68': ' Net Income to Total Assets', 'F33': ' Realized Sales Gross Profit Growth Rate', 'F20': ' Accounts Receivable Turnover', 'F86': ' Operating Expense Rate', 'F87': ' Contingent liabilities\\/Net worth', 'F85': ' Non-industry income and expenditure\\/revenue', 'F78': ' Current Ratio', 'F26': ' Cash Flow to Liability', 'F16': ' Fixed Assets Turnover Frequency', 'F34': ' Regular Net Profit Growth Rate', 'F66': ' Quick Asset Turnover Rate', 'F19': ' Net Value Per Share (C)', 'F76': ' Operating Profit Growth Rate', 'F79': ' After-tax Net Profit Growth Rate', 'F82': ' Continuous Net Profit Growth Rate', 'F24': ' Net Value Per Share (B)', 'F50': ' Equity to Long-term Liability', 'F81': ' CFO to Assets', 'F55': ' Total debt\\/Total net worth', 'F71': ' Current Asset Turnover Rate', 'F12': " Net Income to Stockholder's Equity", 'F83': ' Operating Gross Margin', 'F11': ' Operating Profit Per Share (Yuan ¥)', 'F60': ' Operating Profit Rate', 'F15': ' Cash Flow Per Share', 'F77': ' Total income\\/Total expense', 'F67': ' No-credit Interval', 'F35': ' Liability to Equity', 'F89': ' Working Capital to Total Assets', 'F45': ' Working Capital\\/Equity', 'F57': ' Long-term Liability to Current Assets', 'F43': ' Interest-bearing debt interest rate', 'F52': ' Inventory and accounts receivable\\/Net value', 'F90': ' Realized Sales Gross Margin', 'F61': ' Current Liability to Equity', 'F62': ' Equity to Liability', 'F65': ' Current Liability to Liability', 'F44': ' Operating profit\\/Paid-in capital', 'F64': ' Operating Funds to Liability', 'F5': ' Current Liability to Current Assets', 'F10': ' Net worth\\/Assets', 'F6': ' Tax rate (A)', 'F70': ' Quick Assets\\/Current Liability', 'F38': ' After-tax net Interest Rate', 'F18': ' Per Share Net profit before tax (Yuan ¥)', 'F63': ' Total Asset Turnover', 'F41': ' Cash Reinvestment %', 'F14': ' Fixed Assets to Assets', 'F42': ' Working capitcal Turnover Rate', 'F1': ' Net profit before tax\\/Paid-in capital', 'F17': ' Net Worth Turnover Rate (times)', 'F88': ' Debt ratio %', 'F22': ' Cash Flow to Equity', 'F54': ' Long-term fund suitability ratio (A)', 'F4': ' Cash Flow to Sales', 'F39': ' Total Asset Growth Rate', 'F69': ' Inventory\\/Current Liability', 'F58': ' Allocation rate per person', 'F23': ' Inventory Turnover Rate (times)', 'F56': ' Operating profit per person', 'F49': ' Net Value Growth Rate', 'F84': ' Interest Expense Ratio', 'F72': ' ROA(B) before interest and depreciation after tax', 'F73': ' Continuous interest rate (after tax)', 'F92': ' Inventory\\/Working Capital', 'F48': ' Retained Earnings to Total Assets', 'F37': ' Total assets to GNP price', 'F21': ' Persistent EPS in the Last Four Seasons', 'F8': ' Quick Ratio', 'F74': ' Revenue per person', 'F28': ' Borrowing dependency', 'F13': ' Cash\\/Total Assets', 'F25': ' ROA(A) before interest and % after tax', 'F2': ' ROA(C) before interest and depreciation before interest', 'F36': ' Average Collection Days', 'F31': ' Current Liabilities\\/Liability', 'F53': ' Cash Flow to Total Assets', 'F80': ' Pre-tax net Interest Rate', 'F46': ' Current Liability to Assets', 'F30': ' Quick Assets\\/Total Assets', 'F59': ' Total expense\\/Assets', 'F7': ' Net Value Per Share (A)', 'F9': ' Current Assets\\/Total Assets', 'F93': ' Research and development expense rate', 'F40': ' Current Liabilities\\/Equity', 'F27': ' Cash flow rate', 'F75': ' Total Asset Return Growth Rate Ratio', 'F51': ' Degree of Financial Leverage (DFL)', 'F3': ' Cash Turnover Rate', 'F91': ' Cash\\/Current Liability', 'F29': ' Revenue Per Share (Yuan ¥)', 'F32': ' Gross Profit to Sales'} | {'F60': 'F47', 'F16': 'F68', 'F38': 'F33', 'F2': 'F20', 'F19': 'F86', 'F64': 'F87', 'F4': 'F85', 'F82': 'F78', 'F50': 'F26', 'F22': 'F16', 'F85': 'F34', 'F33': 'F66', 'F88': 'F19', 'F43': 'F76', 'F80': 'F79', 'F54': 'F82', 'F27': 'F24', 'F23': 'F50', 'F76': 'F81', 'F7': 'F55', 'F61': 'F71', 'F59': 'F12', 'F62': 'F83', 'F63': 'F11', 'F58': 'F60', 'F65': 'F15', 'F57': 'F77', 'F56': 'F67', 'F66': 'F35', 'F67': 'F89', 'F68': 'F45', 'F69': 'F57', 'F1': 'F43', 'F70': 'F52', 'F83': 'F90', 'F92': 'F61', 'F91': 'F62', 'F90': 'F65', 'F89': 'F44', 'F87': 'F64', 'F86': 'F5', 'F84': 'F10', 'F81': 'F6', 'F71': 'F70', 'F79': 'F38', 'F78': 'F18', 'F77': 'F63', 'F75': 'F41', 'F74': 'F14', 'F73': 'F42', 'F72': 'F1', 'F55': 'F17', 'F47': 'F88', 'F53': 'F22', 'F52': 'F54', 'F25': 'F4', 'F24': 'F39', 'F21': 'F69', 'F20': 'F58', 'F18': 'F23', 'F17': 'F56', 'F15': 'F49', 'F14': 'F84', 'F13': 'F72', 'F12': 'F73', 'F11': 'F92', 'F10': 'F48', 'F9': 'F37', 'F8': 'F21', 'F6': 'F8', 'F5': 'F74', 'F3': 'F28', 'F26': 'F13', 'F28': 'F25', 'F29': 'F2', 'F41': 'F36', 'F51': 'F31', 'F49': 'F53', 'F48': 'F80', 'F46': 'F46', 'F45': 'F30', 'F44': 'F59', 'F42': 'F7', 'F40': 'F9', 'F30': 'F93', 'F39': 'F40', 'F37': 'F27', 'F36': 'F75', 'F35': 'F51', 'F34': 'F3', 'F32': 'F91', 'F31': 'F29', 'F93': 'F32'} | {'C1': 'C1', 'C2': 'C2'} | No | {'C1': 'No', 'C2': 'Yes'} |
BernoulliNB | C2 | Student Job Placement | For the case under consideration, the model assigned C2 with very high confidence, since the likelihood of C1 being the right label is only 0.52% which is very small. F6, F3, F4, and F12 have a large positive impact on the model's output prediction. F4 and F12 have a moderately positive impact on the prediction of C2, while F11 has a similar impact but in the opposite direction. F10, F5, and F2 have a very low impact on classification. F1, F8, F9, and F7 have a larger but still insignificant effect. Examining the attributions indicates that there are only two features, F11 and F5, with values that contradict the prediction made here but, their impact on the model is smaller when compared to positive features such as F3, F4, and F6, which explains why the confidence level associated with this classification is high. | [
"0.33",
"0.31",
"0.21",
"0.15",
"-0.13",
"0.08",
"0.06",
"0.04",
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"positive",
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"positive",
"negative",
"positive",
"positive",
"positive",
"positive",
"negative",
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] | 21 | 2,304 | {'C1': '0.52%', 'C2': '99.48%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F4, F12, F11 and F1 (equal to V1)) on the model’s prediction of C2.",
"Summarize the set of features has little to no impact on the prediction?"
] | [
"F6",
"F3",
"F4",
"F12",
"F11",
"F1",
"F7",
"F9",
"F8",
"F5",
"F2",
"F10"
] | {'F6': 'workex', 'F3': 'specialisation', 'F4': 'ssc_p', 'F12': 'hsc_p', 'F11': 'degree_p', 'F1': 'gender', 'F7': 'degree_t', 'F9': 'etest_p', 'F8': 'hsc_b', 'F5': 'hsc_s', 'F2': 'ssc_b', 'F10': 'mba_p'} | {'F11': 'F6', 'F12': 'F3', 'F1': 'F4', 'F2': 'F12', 'F3': 'F11', 'F6': 'F1', 'F10': 'F7', 'F4': 'F9', 'F8': 'F8', 'F9': 'F5', 'F7': 'F2', 'F5': 'F10'} | {'C2': 'C1', 'C1': 'C2'} | Placed | {'C1': 'Not Placed', 'C2': 'Placed'} |
LogisticRegression | C1 | Flight Price-Range Classification | Since the likelihood of C1 being the true label is shown by the prediction algorithm outputs to be equal to 93.02 percent, there is only a small chance that the true label for the given data instance is any of the other class labels, C2 and C3. The features F1, F5, F10, and F6 are the most important ones driving the label assignment verdict above, and on the other hand, the least relevant features are shown to be F9, F8, and F4. Considering the direction of influence of each input feature, as shown by the attribution analysis, it can be concluded that the positive features steering the prediction higher towards C1 are F1, F5, F6, F10, F7, F11, and F8. The marginal doubt in the predicted output decision is attributed to the negative contributions of F2, F12, F4, F9, and F3. Considering the attributions of the features and predicted probabilities across the classes, it can be concluded that the joint positive contribution outranks the negative contributions; hence, the algorithm is confident that C1 is likely the true label. | [
"0.41",
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"positive",
"positive",
"positive",
"positive",
"negative",
"negative",
"positive",
"negative",
"positive",
"negative",
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] | 318 | 2,714 | {'C1': '93.02%', 'C2': '6.97%', 'C3': '0.01%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F10, F2 and F12) with moderate impact on the prediction made for this test case."
] | [
"F1",
"F5",
"F6",
"F10",
"F2",
"F12",
"F7",
"F3",
"F11",
"F9",
"F8",
"F4"
] | {'F1': 'Total_Stops', 'F5': 'Airline', 'F6': 'Destination', 'F10': 'Journey_day', 'F2': 'Source', 'F12': 'Dep_hour', 'F7': 'Duration_hours', 'F3': 'Dep_minute', 'F11': 'Duration_mins', 'F9': 'Arrival_minute', 'F8': 'Arrival_hour', 'F4': 'Journey_month'} | {'F12': 'F1', 'F9': 'F5', 'F11': 'F6', 'F1': 'F10', 'F10': 'F2', 'F3': 'F12', 'F7': 'F7', 'F4': 'F3', 'F8': 'F11', 'F6': 'F9', 'F5': 'F8', 'F2': 'F4'} | {'C3': 'C1', 'C2': 'C2', 'C1': 'C3'} | Low | {'C1': 'Low', 'C2': 'Moderate', 'C3': 'High'} |
RandomForestClassifier | C2 | Cab Surge Pricing System | Between the three possible classes, there is an 88.0% probability that the correct label for this case is C2. This means that there is a 12.0% chance that the label could be one of the other possible labels, C3 or C1. Increasing the odds of the predicted label are the variables F10, F12, F8, and F2. The next set of variables, F7, F9, and F11, have values that moderately decrease the likelihood of C2 being the correct label. F4, F6, and F1 are the other negatively contributing features, and given that they are lowly ranked, they have a marginal impact when determining the correct label for this case. The other positive features further increasing the probability that C2 is the right label are F3 and F5. Overall, we can conclude that the decision to label the case as C2 is largely due to the strong positive influence of F12, F10, F2, and F8. | [
"0.27",
"0.05",
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"-0.03",
"-0.02",
"0.02",
"0.01",
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"positive",
"positive",
"positive",
"positive",
"negative",
"negative",
"negative",
"positive",
"positive",
"negative",
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"negative"
] | 171 | 2,734 | {'C3': '3.00%', 'C1': '9.00%', 'C2': '88.00%'} | [
"Summarize the prediction for the given test example?",
"For this test case, summarize the top features influencing the model's decision.",
"For these top features, what are the respective directions of influence on the prediction?",
"Provide a statement on the set of features has limited impact on the prediction of C2 by the model for the given test example?"
] | [
"F10",
"F12",
"F8",
"F2",
"F7",
"F9",
"F11",
"F3",
"F5",
"F4",
"F6",
"F1"
] | {'F10': 'Type_of_Cab', 'F12': 'Destination_Type', 'F8': 'Cancellation_Last_1Month', 'F2': 'Trip_Distance', 'F7': 'Customer_Rating', 'F9': 'Life_Style_Index', 'F11': 'Var3', 'F3': 'Var1', 'F5': 'Customer_Since_Months', 'F4': 'Var2', 'F6': 'Gender', 'F1': 'Confidence_Life_Style_Index'} | {'F2': 'F10', 'F6': 'F12', 'F8': 'F8', 'F1': 'F2', 'F7': 'F7', 'F4': 'F9', 'F11': 'F11', 'F9': 'F3', 'F3': 'F5', 'F10': 'F4', 'F12': 'F6', 'F5': 'F1'} | {'C2': 'C3', 'C3': 'C1', 'C1': 'C2'} | C3 | {'C3': 'Low', 'C1': 'Medium', 'C2': 'High'} |
RandomForestClassifier | C2 | Wine Quality Prediction | Based on the input variables, the model is moderately confident that the C2 is the appropriate label for the data under consideration. As a matter of fact, the prediction likelihood associated with class C1 is about 30.42%. The preceeding classification verdict can be largely blamed on the contributions of variables F2, F7, F10, and F1, whereas those with marginally lower contributions are F4, F5, and F6. The variables with moderate contributions are F11, F9, F8, and F3. Considering their respective contributions, F2, F10, F1, and F3 are the variables with positive influence that increase the chances of C2 being the correct label for the given data. The little doubt in the label choice here could be attributed to the negative variables, mainly F7, F11, F8, and F9, which decrease the chances of the model labelling the data given as C2 since these negative variables favour selecting the alternative label, C1 over C2. Given that majority of top variables contribute positively, it is not unexpected that C2 is the picked label with reasonably high confidence. | [
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"0.04",
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"-0.03",
"-0.03",
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] | [
"positive",
"negative",
"positive",
"positive",
"negative",
"negative",
"negative",
"positive",
"negative",
"negative",
"negative"
] | 404 | 2,490 | {'C1': '30.42%', 'C2': '69.58%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F10, F1, F11 and F9) with moderate impact on the prediction made for this test case."
] | [
"F2",
"F7",
"F10",
"F1",
"F11",
"F9",
"F8",
"F3",
"F4",
"F5",
"F6"
] | {'F2': 'alcohol', 'F7': 'sulphates', 'F10': 'volatile acidity', 'F1': 'total sulfur dioxide', 'F11': 'fixed acidity', 'F9': 'citric acid', 'F8': 'residual sugar', 'F3': 'density', 'F4': 'chlorides', 'F5': 'pH', 'F6': 'free sulfur dioxide'} | {'F11': 'F2', 'F10': 'F7', 'F2': 'F10', 'F7': 'F1', 'F1': 'F11', 'F3': 'F9', 'F4': 'F8', 'F8': 'F3', 'F5': 'F4', 'F9': 'F5', 'F6': 'F6'} | {'C1': 'C1', 'C2': 'C2'} | high quality | {'C1': 'low_quality', 'C2': 'high quality'} |
SVC | C2 | E-Commerce Shipping | The classifier is 69.02% certain that the given case is under the class label C2, implying that the likelihood of C1 is only 30.98%. Analysis performed to understand the contribution of each input feature revealed that: F5, F8, and F7 are the most influential features when assigning a label to the given case. Features F9, F2, F3, and F10 have moderate contributions, whereas the F6, F1 and F4 have lower relevance to the final classification decision. F5 and F7 push the class assignment towards C2, whereas F8 does the opposite, decreasing the likelihood of C2. Similar to F8, F9, and F2 negatively impact the C2 classification, whereas F10, F6, and F3 positively push the decision towards the C2 class. Features F1, and F4 all have little impact on the final decision, with F4 having the least impact. | [
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"0.10",
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"0.01",
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] | [
"positive",
"negative",
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"negative",
"negative",
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] | 53 | 2,717 | {'C2': '69.02%', 'C1': '30.98%'} | [
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Summarize the direction of influence of the features (F5 and F8) on the prediction made for this test case.",
"Compare the direction of impact of the features: F7 (value equal to V3), F9 (when it is equal to V1), F2 and F3 (when it is equal to V2).",
"Describe the degree of impact of the following features: F10 (with a value equal to V1), F6 (with a value equal to V0), F1 (when it is equal to V1) and F4 (with a value equal to V4)?"
] | [
"F5",
"F8",
"F7",
"F9",
"F2",
"F3",
"F10",
"F6",
"F1",
"F4"
] | {'F5': 'Weight_in_gms', 'F8': 'Discount_offered', 'F7': 'Prior_purchases', 'F9': 'Customer_care_calls', 'F2': 'Cost_of_the_Product', 'F3': 'Mode_of_Shipment', 'F10': 'Customer_rating', 'F6': 'Gender', 'F1': 'Product_importance', 'F4': 'Warehouse_block'} | {'F3': 'F5', 'F2': 'F8', 'F8': 'F7', 'F6': 'F9', 'F1': 'F2', 'F5': 'F3', 'F7': 'F10', 'F10': 'F6', 'F9': 'F1', 'F4': 'F4'} | {'C1': 'C2', 'C2': 'C1'} | On-time | {'C2': 'On-time', 'C1': 'Late'} |
RandomForestClassifier | C1 | Advertisement Prediction | The classifier trained on this prediction problem assigns a label to a given case based on the information supplied. The class assigned by the classifier to the case under consideration is C1. The probability that C2 is the correct label is around 25.28%; therefore, it is less likely to be the true label. The above classification decision is mainly based on the influence of the features F1, F3, F5, F2, F4, F7, and F6. Of the above stated features, F2 and F3 are the ones shown to have a negative impact, decreasing the odds of C1 being the accurate label for the given case and encouraging the classifier to select C2 instead. Finally, it can be concluded that there is a moderately high level of confidence in the assigned label, which can be attributed to the strong positive contribution of F1 combined with other positive features such as F5 and F4. | [
"0.23",
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"0.03",
"-0.03",
"0.02",
"0.02",
"0.01"
] | [
"positive",
"negative",
"positive",
"negative",
"positive",
"positive",
"positive"
] | 31 | 2,681 | {'C1': '74.72%', 'C2': '25.28%'} | [
"Provide a statement summarizing the prediction made for the test case.",
"For the current test instance, describe the direction of influence of the following features: F1 and F3.",
"Compare and contrast the impact of the following features (F5, F2 (when it is equal to V1), F4 and F7 (when it is equal to V1)) on the model’s prediction of C1.",
"Describe the degree of impact of the following features: F6 (with a value equal to V4)?"
] | [
"F1",
"F3",
"F5",
"F2",
"F4",
"F7",
"F6"
] | {'F1': 'Daily Time Spent on Site', 'F3': 'Daily Internet Usage', 'F5': 'Age', 'F2': 'ad_day', 'F4': 'Area Income', 'F7': 'Gender', 'F6': 'ad_month'} | {'F1': 'F1', 'F4': 'F3', 'F2': 'F5', 'F7': 'F2', 'F3': 'F4', 'F5': 'F7', 'F6': 'F6'} | {'C1': 'C1', 'C2': 'C2'} | Skip | {'C1': 'Skip', 'C2': 'Watch'} |
GradientBoostingClassifier | C1 | Food Ordering Customer Churn Prediction | The case given is labelled as C1 by the classifier with a confidence level equal to 82.07%. Therefore, the probability of C2 being the correct label is only 17.93%. The classification above is mainly due to the contributions of features such as F46, F31, F30, and F39. F25, F15, and F38 are the next three with moderate influence. However, not all the features are considered by the classifier when determining the correct label for the given case. F16, F14, F42, and F37 are notable irrelevant features. With regards to the direction of influence of the relevant features, F46, F31, F30, and F39 are the top features with strong positive contributions favouring the assignment of label C1. The top negative features that shift the classification in a different direction are F25, F15, F43, and F23. Considering the fact that a number of the relevant features have positive attributions, it is not surprising that the classifier is quite certain that the appropriate label is C1 instead of C2. | [
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] | 258 | 2,464 | {'C2': '17.93%', 'C1': '82.07%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F30, F39, F25 and F15) with moderate impact on the prediction made for this test case."
] | [
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] | {'F46': 'More restaurant choices', 'F31': 'Ease and convenient', 'F30': 'Bad past experience', 'F39': 'Time saving', 'F25': 'Easy Payment option', 'F15': 'Good Tracking system', 'F38': 'Wrong order delivered', 'F43': 'Influence of rating', 'F21': 'Late Delivery', 'F23': 'Less Delivery time', 'F1': 'Long delivery time', 'F13': 'Delivery person ability', 'F27': 'Order placed by mistake', 'F6': 'More Offers and Discount', 'F26': 'Freshness ', 'F24': 'Unavailability', 'F28': 'Delay of delivery person picking up food', 'F20': 'Poor Hygiene', 'F34': 'Order Time', 'F41': 'Delay of delivery person getting assigned', 'F16': 'High Quality of package', 'F14': 'Residence in busy location', 'F42': 'Good Taste ', 'F37': 'Temperature', 'F33': 'Google Maps Accuracy', 'F2': 'Good Road Condition', 'F9': 'Number of calls', 'F11': 'Low quantity low time', 'F44': 'Politeness', 'F29': 'Maximum wait time', 'F17': 'Age', 'F18': 'Influence of time', 'F7': 'Missing item', 'F4': 'Family size', 'F8': 'Unaffordable', 'F22': 'Health Concern', 'F12': 'Self Cooking', 'F45': 'Good Food quality', 'F5': 'Perference(P2)', 'F40': 'Perference(P1)', 'F19': 'Educational Qualifications', 'F35': 'Monthly Income', 'F32': 'Occupation', 'F36': 'Marital Status', 'F3': 'Gender', 'F10': 'Good Quantity'} | {'F12': 'F46', 'F10': 'F31', 'F21': 'F30', 'F11': 'F39', 'F13': 'F25', 'F16': 'F15', 'F27': 'F38', 'F38': 'F43', 'F19': 'F21', 'F39': 'F23', 'F24': 'F1', 'F37': 'F13', 'F29': 'F27', 'F14': 'F6', 'F43': 'F26', 'F22': 'F24', 'F26': 'F28', 'F20': 'F20', 'F31': 'F34', 'F25': 'F41', 'F40': 'F16', 'F33': 'F14', 'F45': 'F42', 'F44': 'F37', 'F34': 'F33', 'F35': 'F2', 'F41': 'F9', 'F36': 'F11', 'F42': 'F44', 'F32': 'F29', 'F1': 'F17', 'F30': 'F18', 'F28': 'F7', 'F7': 'F4', 'F23': 'F8', 'F18': 'F22', 'F17': 'F12', 'F15': 'F45', 'F9': 'F5', 'F8': 'F40', 'F6': 'F19', 'F5': 'F35', 'F4': 'F32', 'F3': 'F36', 'F2': 'F3', 'F46': 'F10'} | {'C2': 'C2', 'C1': 'C1'} | Go Away | {'C2': 'Return', 'C1': 'Go Away'} |
SVM_linear | C2 | Mobile Price-Range Classification | According to the algorithm, there is little to no chance that the correct label for the given data instance is any of the following classes: C1, C4, and C3. It is very confident that the proper label is C2. This label assignment is largely due to the parts played by the features F1, F16, and F4. On the lower end are the input features F18, F11, F8, and F3, which are shown to be less relevant when it comes to this labelling assignment task. Finally, among the top features identified during the attribution investogation, only F19 and F12 are features with a negative influence, decreasing the odds of C2 being the appropriate label here. | [
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"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F6, F5 and F13?"
] | [
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] | {'F1': 'ram', 'F16': 'battery_power', 'F4': 'px_width', 'F12': 'int_memory', 'F19': 'sc_h', 'F14': 'pc', 'F10': 'mobile_wt', 'F6': 'fc', 'F5': 'n_cores', 'F13': 'clock_speed', 'F17': 'blue', 'F15': 'three_g', 'F9': 'touch_screen', 'F7': 'm_dep', 'F20': 'px_height', 'F2': 'talk_time', 'F18': 'dual_sim', 'F11': 'wifi', 'F8': 'four_g', 'F3': 'sc_w'} | {'F11': 'F1', 'F1': 'F16', 'F10': 'F4', 'F4': 'F12', 'F12': 'F19', 'F8': 'F14', 'F6': 'F10', 'F3': 'F6', 'F7': 'F5', 'F2': 'F13', 'F15': 'F17', 'F18': 'F15', 'F19': 'F9', 'F5': 'F7', 'F9': 'F20', 'F14': 'F2', 'F16': 'F18', 'F20': 'F11', 'F17': 'F8', 'F13': 'F3'} | {'C4': 'C1', 'C1': 'C4', 'C3': 'C3', 'C2': 'C2'} | r4 | {'C1': 'r1', 'C4': 'r2', 'C3': 'r3', 'C2': 'r4'} |
SVC | C1 | Paris House Classification | The model predicts that the label for this case is C1 with a high degree of certainty of about 99.19% and the probability of the other label is only 0.81%. From the analysis, the variables with the strongest attributions to this classification decision are F9, F5, and F11. The attributions of these variables increased the response of the model in favour of labelling the case as C1. Other variables that positively supported the label decision include F3, F7, and F14. Not all the variables support the model's prediction of C1 and this is because the values of F8, F1, F10, F2, and F4 are driving the prediction towards C2. The joint attribution from these variables is weaker than that from F9, F5, and F11, so the model is biased toward predicting C1. Finally, F12, F16, F6, and F17 are the least important positive features, given that they have minimal attributions in favour of C1. | [
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] | 168 | 2,390 | {'C1': '99.19%', 'C2': '0.81%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F5, F8, F1 and F3) on the model’s prediction of C1.",
"Summarize the set of features has little to no impact on the prediction?"
] | [
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"F14",
"F10",
"F13",
"F2",
"F15",
"F4",
"F12",
"F16",
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] | {'F9': 'isNewBuilt', 'F11': 'hasYard', 'F5': 'hasPool', 'F8': 'hasStormProtector', 'F1': 'hasStorageRoom', 'F3': 'made', 'F7': 'basement', 'F14': 'numberOfRooms', 'F10': 'squareMeters', 'F13': 'floors', 'F2': 'numPrevOwners', 'F15': 'garage', 'F4': 'attic', 'F12': 'cityCode', 'F16': 'price', 'F6': 'cityPartRange', 'F17': 'hasGuestRoom'} | {'F3': 'F9', 'F1': 'F11', 'F2': 'F5', 'F4': 'F8', 'F5': 'F1', 'F12': 'F3', 'F13': 'F7', 'F7': 'F14', 'F6': 'F10', 'F8': 'F13', 'F11': 'F2', 'F15': 'F15', 'F14': 'F4', 'F9': 'F12', 'F17': 'F16', 'F10': 'F6', 'F16': 'F17'} | {'C2': 'C1', 'C1': 'C2'} | Basic | {'C1': 'Basic', 'C2': 'Luxury'} |
LogisticRegression | C1 | Used Cars Price-Range Prediction | According to the output prediction probabilities across the two classes, the output decision for the given data is C1 with a very high confidence level. C2 has a prediction probability of about 0.00%. The variables contributing most to the abovementioned classification are F7, F2, and F3, whereas F8 and F10 are the least influential variables. The very high confidence level associated with the classification decision here could be attributed to the fact that a greater number of the input variables have attributions that increase the model's response towards label C1. F4, F5, and F8 are the variables with negative contributions that attempt to push the model to label this case as C2. To put it in a nutshell, the joint contribution of the negative variables is very low unlike that of the positive variables, hence the model's certainty in the decision here. | [
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"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F8 and F10?"
] | [
"F7",
"F2",
"F3",
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"F9",
"F1",
"F4",
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] | {'F7': 'car_age', 'F2': 'Power', 'F3': 'Fuel_Type', 'F6': 'Engine', 'F9': 'Seats', 'F1': 'Transmission', 'F4': 'Kilometers_Driven', 'F5': 'Name', 'F8': 'Mileage', 'F10': 'Owner_Type'} | {'F5': 'F7', 'F4': 'F2', 'F7': 'F3', 'F3': 'F6', 'F10': 'F9', 'F8': 'F1', 'F1': 'F4', 'F6': 'F5', 'F2': 'F8', 'F9': 'F10'} | {'C2': 'C1', 'C1': 'C2'} | Low | {'C1': 'Low', 'C2': 'High'} |
SVC | C1 | Tic-Tac-Toe Strategy | With a labelling confidence level of 99.50%, the classifier predicts the label C1 in this situation. Hence, it is correct to conclude that the classifier is less certain that C2 is the proper label for the case here. The analysis indicates that five features contradict the decision above, while four features support the classifier. The features contradicting the prediction are usually referred to as negative features while those supporting it are referred to as positive features. The negative features decreasing the odds of C1 being the correct label are F6, F9, F7, F4, and F1. Conversely, the positive features increasing the odds of C1 are F8, F3, F5, and F2. | [
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] | 202 | 2,413 | {'C2': '0.50%', 'C1': '99.50%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F4, F2 and F1?"
] | [
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"F5",
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"F4",
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] | {'F6': 'middle-middle-square', 'F8': 'top-left-square', 'F3': 'bottom-left-square', 'F5': 'bottom-right-square', 'F9': ' top-right-square', 'F7': 'middle-right-square', 'F4': 'top-middle-square', 'F2': 'middle-left-square', 'F1': 'bottom-middle-square'} | {'F5': 'F6', 'F1': 'F8', 'F7': 'F3', 'F9': 'F5', 'F3': 'F9', 'F6': 'F7', 'F2': 'F4', 'F4': 'F2', 'F8': 'F1'} | {'C1': 'C2', 'C2': 'C1'} | player B win | {'C2': 'player B lose', 'C1': 'player B win'} |
SVMClassifier_poly | C1 | Employee Attrition | The class assigned by the model is C1 with a close to 97.67% confidence level, implying that the likelihood of C2 is only 2.33%. Based on the analysis, the most important features considered during the classification are F13, F26, F2, and F23 but among these features, F26 and F2 are the only ones with negative attributions, decreasing the likelihood of C1 being the label for the given case. Furthermore, moderately influencing the decision are F17, F28, F29, and F21. F17, F28, and F29 have positive attributions, while F21 has a negative impact, shifting the prediction in a different direction. Finally, the features with insignificant impact on the model when it comes to this case include F24, F22, F20, and F4. | [
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] | 179 | 2,399 | {'C1': '97.67%', 'C2': '2.33%'} | [
"Summarize the prediction for the given test example?",
"For this test case, summarize the top features influencing the model's decision.",
"For these top features, what are the respective directions of influence on the prediction?",
"Provide a statement on the set of features has limited impact on the prediction of C1 by the model for the given test example?"
] | [
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] | {'F13': 'OverTime', 'F26': 'JobSatisfaction', 'F2': 'BusinessTravel', 'F23': 'MaritalStatus', 'F17': 'EnvironmentSatisfaction', 'F28': 'Department', 'F29': 'Age', 'F21': 'YearsInCurrentRole', 'F18': 'TotalWorkingYears', 'F7': 'WorkLifeBalance', 'F6': 'JobLevel', 'F14': 'JobInvolvement', 'F1': 'EducationField', 'F8': 'JobRole', 'F5': 'MonthlyIncome', 'F25': 'PerformanceRating', 'F9': 'DistanceFromHome', 'F30': 'Education', 'F3': 'Gender', 'F19': 'YearsWithCurrManager', 'F24': 'PercentSalaryHike', 'F22': 'RelationshipSatisfaction', 'F20': 'MonthlyRate', 'F4': 'DailyRate', 'F10': 'YearsSinceLastPromotion', 'F16': 'HourlyRate', 'F12': 'YearsAtCompany', 'F11': 'TrainingTimesLastYear', 'F27': 'StockOptionLevel', 'F15': 'NumCompaniesWorked'} | {'F26': 'F13', 'F30': 'F26', 'F17': 'F2', 'F25': 'F23', 'F28': 'F17', 'F21': 'F28', 'F1': 'F29', 'F14': 'F21', 'F11': 'F18', 'F20': 'F7', 'F5': 'F6', 'F29': 'F14', 'F22': 'F1', 'F24': 'F8', 'F6': 'F5', 'F19': 'F25', 'F3': 'F9', 'F27': 'F30', 'F23': 'F3', 'F16': 'F19', 'F9': 'F24', 'F18': 'F22', 'F7': 'F20', 'F2': 'F4', 'F15': 'F10', 'F4': 'F16', 'F13': 'F12', 'F12': 'F11', 'F10': 'F27', 'F8': 'F15'} | {'C2': 'C1', 'C1': 'C2'} | Stay | {'C1': 'Leave', 'C2': 'Leave'} |
KNeighborsClassifier | C2 | Advertisement Prediction | With a higher degree of confidence, the model labels this given case as C2 since there is a zero chance that it is C1. The classification here can be attributed to all the features having positive contributions, decreasing the odds of C1 being the correct label. The features can be ranked based on their degree of influence from the most relevant to the least relevant as follows: F4, F5, F2, F7, F6, F1, F3. This implies that F4 is the most influential feature, while F3 is the least influential among the input features. | [
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] | [
"positive",
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] | 253 | 2,459 | {'C1': '0.00%', 'C2': '100.00%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F3?"
] | [
"F4",
"F5",
"F2",
"F7",
"F6",
"F1",
"F3"
] | {'F4': 'Daily Time Spent on Site', 'F5': 'Area Income', 'F2': 'Age', 'F7': 'Daily Internet Usage', 'F6': 'ad_day', 'F1': 'Gender', 'F3': 'ad_month'} | {'F1': 'F4', 'F3': 'F5', 'F2': 'F2', 'F4': 'F7', 'F7': 'F6', 'F5': 'F1', 'F6': 'F3'} | {'C2': 'C1', 'C1': 'C2'} | Watch | {'C1': 'Skip', 'C2': 'Watch'} |
SVM_poly | C2 | Mobile Price-Range Classification | According to the model, C2 has a prediction probability of 99.45 percent, C4 has a prediction probability of 0.47 percent, C1 has a prediction probability of 0.04 percent, and C3 has a prediction probability of 0.05 percent, therefore, the most likely class is C2. F17 and F4 positively influence the above-mentioned label decision in favour of C2, but F2 has the opposite effect, favouring a different label. F9 and F7 both have a similar negative impact on the C2 prediction, whereas F14 has a positive impact. In this case, F11, F20, F8, and F12 have little influence on the labelling result. All in all, the model is confident in its assignment of the C2 class as shown by the predicted probabilities across the classes. | [
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"Provide a statement summarizing the prediction made for the test case.",
"For the current test instance, describe the direction of influence of the following features: F4, F17 and F2.",
"Compare and contrast the impact of the following features (F9, F14 (value equal to V1) and F7 (value equal to V1)) on the model’s prediction of C2.",
"Describe the degree of impact of the following features: F11 (when it is equal to V0), F20, F8 and F12?"
] | [
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"F3"
] | {'F4': 'ram', 'F17': 'battery_power', 'F2': 'px_height', 'F9': 'px_width', 'F14': 'dual_sim', 'F7': 'four_g', 'F11': 'touch_screen', 'F20': 'int_memory', 'F8': 'pc', 'F12': 'n_cores', 'F13': 'fc', 'F15': 'clock_speed', 'F16': 'three_g', 'F10': 'sc_w', 'F19': 'wifi', 'F1': 'm_dep', 'F5': 'mobile_wt', 'F18': 'talk_time', 'F6': 'sc_h', 'F3': 'blue'} | {'F11': 'F4', 'F1': 'F17', 'F9': 'F2', 'F10': 'F9', 'F16': 'F14', 'F17': 'F7', 'F19': 'F11', 'F4': 'F20', 'F8': 'F8', 'F7': 'F12', 'F3': 'F13', 'F2': 'F15', 'F18': 'F16', 'F13': 'F10', 'F20': 'F19', 'F5': 'F1', 'F6': 'F5', 'F14': 'F18', 'F12': 'F6', 'F15': 'F3'} | {'C1': 'C2', 'C2': 'C4', 'C4': 'C1', 'C3': 'C3'} | r1 | {'C2': 'r1', 'C4': 'r2', 'C1': 'r3', 'C3': 'r4'} |
GradientBoostingClassifier | C1 | Food Ordering Customer Churn Prediction | Per the model employed here, the prediction probability of C2 is only 17.93%, and that of C1 is equal to 82.07%. Given the information provided to the model, the most valid conclusion regarding the true label is that C1 is without a doubt the most likely one. The attributions analysis indicates that F26, F20, F28, F46, and F17 are the major drivers resulting in the prediction probabilities across the classes under consideration. At the tail end are features such as F38, F27, F30, and F9 that have very little influence on the decision made with respect to the given case. Among the influential features, only F26, F20, F46, F24, F42, F39, F41, F40, and F16 have positive contributions in support of labelling the given case as C1. On the other hand, the negative features such as F28, F17, F14, F22, F29, F1, and F33, suggest C2 could likely be the true label in this case. Overall, the marginal doubt in the correctness of assigning C1 to the case under consideration is attributed to the negative features driving the model's decision in the direction of C2 away from C1. But the higher influence of positive features such as F26 and F20 ensures that C1 is assigned as the most probable label. | [
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"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F17, F14 and F24) on the model’s prediction of C1.",
"Summarize the set of features has little to no impact on the prediction?"
] | [
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] | {'F26': 'Ease and convenient', 'F20': 'More restaurant choices', 'F28': 'Bad past experience', 'F46': 'More Offers and Discount', 'F17': 'Unavailability', 'F14': 'Good Food quality', 'F24': 'Low quantity low time', 'F42': 'Delay of delivery person getting assigned', 'F22': 'Late Delivery', 'F39': 'Less Delivery time', 'F33': 'Residence in busy location', 'F29': 'Freshness ', 'F1': 'Educational Qualifications', 'F41': 'Influence of rating', 'F35': 'Occupation', 'F19': 'Perference(P1)', 'F32': 'Delivery person ability', 'F40': 'Good Taste ', 'F16': 'Long delivery time', 'F45': 'Self Cooking', 'F38': 'Influence of time', 'F27': 'High Quality of package', 'F30': 'Number of calls', 'F9': 'Good Road Condition', 'F3': 'Politeness', 'F13': 'Google Maps Accuracy', 'F2': 'Temperature', 'F31': 'Maximum wait time', 'F10': 'Order Time', 'F23': 'Age', 'F7': 'Order placed by mistake', 'F21': 'Missing item', 'F34': 'Wrong order delivered', 'F18': 'Delay of delivery person picking up food', 'F8': 'Family size', 'F6': 'Unaffordable', 'F15': 'Poor Hygiene', 'F4': 'Health Concern', 'F12': 'Good Tracking system', 'F43': 'Easy Payment option', 'F11': 'Time saving', 'F5': 'Perference(P2)', 'F44': 'Monthly Income', 'F25': 'Marital Status', 'F36': 'Gender', 'F37': 'Good Quantity'} | {'F10': 'F26', 'F12': 'F20', 'F21': 'F28', 'F14': 'F46', 'F22': 'F17', 'F15': 'F14', 'F36': 'F24', 'F25': 'F42', 'F19': 'F22', 'F39': 'F39', 'F33': 'F33', 'F43': 'F29', 'F6': 'F1', 'F38': 'F41', 'F4': 'F35', 'F8': 'F19', 'F37': 'F32', 'F45': 'F40', 'F24': 'F16', 'F17': 'F45', 'F30': 'F38', 'F40': 'F27', 'F41': 'F30', 'F35': 'F9', 'F42': 'F3', 'F34': 'F13', 'F44': 'F2', 'F32': 'F31', 'F31': 'F10', 'F1': 'F23', 'F29': 'F7', 'F28': 'F21', 'F27': 'F34', 'F26': 'F18', 'F7': 'F8', 'F23': 'F6', 'F20': 'F15', 'F18': 'F4', 'F16': 'F12', 'F13': 'F43', 'F11': 'F11', 'F9': 'F5', 'F5': 'F44', 'F3': 'F25', 'F2': 'F36', 'F46': 'F37'} | {'C1': 'C1', 'C2': 'C2'} | Return | {'C1': 'Return', 'C2': 'Go Away'} |
RandomForestClassifier | C1 | Personal Loan Modelling | The model is about 90.0% certain or sure that the correct label based on the input features of the given case is C1. The features with the most significant influence on the decision are F9, F8, F6, and F7. The influence of the features can be categorised as positive or negative traits depending on the direction of the effect on the model. Positive features increase the likelihood of the most likely class (i.e., C1), whereas negative features reduce the model's responsiveness to the assigned label, favouring the less likely class (i.e., C2). From the attribution analysis, F2, F1, and F5 are the negative features here. Overall, the negative features are shown to have moderate to low influence compared to the positive features, hence explaining why the model is very confident about the assigned label C1. | [
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"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F3, F2 and F5?"
] | [
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] | {'F8': 'Income', 'F9': 'CCAvg', 'F6': 'CD Account', 'F7': 'Education', 'F1': 'Extra_service', 'F4': 'Securities Account', 'F3': 'Family', 'F2': 'Mortgage', 'F5': 'Age'} | {'F2': 'F8', 'F4': 'F9', 'F8': 'F6', 'F5': 'F7', 'F9': 'F1', 'F7': 'F4', 'F3': 'F3', 'F6': 'F2', 'F1': 'F5'} | {'C2': 'C2', 'C1': 'C1'} | Accept | {'C2': 'Reject', 'C1': 'Accept'} |
LogisticRegression | C1 | Tic-Tac-Toe Strategy | With an 81.01% chance of being correct, C1 is the most likely label, consequently, the C2 class's prediction probability is only 18.99%. The algorithm or classifier got the above prediction mostly due to the influence of features like F2, F8, F4, and F7. F9, which is found to have very little impact with regard to the label choice here, is the least relevant feature for the algorithm. F8, F1, F4, and F7 have a positive direction of influence, pushing the algorithm higher towards the C1 label. Negative features like F2, F5, and F3 favour choosing or labelling the case as C2. | [
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] | 231 | 2,603 | {'C2': '18.99%', 'C1': '81.01%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F4, F7, F1 and F3) with moderate impact on the prediction made for this test case."
] | [
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] | {'F8': 'bottom-right-square', 'F2': 'middle-middle-square', 'F4': 'bottom-left-square', 'F7': 'middle-left-square', 'F1': 'top-left-square', 'F3': ' top-right-square', 'F5': 'middle-right-square', 'F6': 'top-middle-square', 'F9': 'bottom-middle-square'} | {'F9': 'F8', 'F5': 'F2', 'F7': 'F4', 'F4': 'F7', 'F1': 'F1', 'F3': 'F3', 'F6': 'F5', 'F2': 'F6', 'F8': 'F9'} | {'C2': 'C2', 'C1': 'C1'} | player B win | {'C2': 'player B lose', 'C1': 'player B win'} |
SVC | C2 | Student Job Placement | The model makes classification decisions based on the information provided to it and for the case here, the prediction probabilities across the two class labels, C1 and C2, are 49.32% and 50.68%, respectively. Based on these prediction probabilities, the label assigned is C2, since it has the highest likelihood, however, the model is not very certain about the correctness of the assigned label since its probability is marginally higher than the average. The uncertainty in the classification here can be blamed on the fact that only F10, F6, F1, F2, and F7 have positive attributions, shifting the decision higher towards C2. On the other hand, features F5, F11, F9, F12, F8, F4, and F3 have negative contributions that decrease the prediction likelihood of C2 while increasing that of C1. To cut a long story short, the most positive features are F10 and F6, whereas the most negative ones are F5 and F11. Finally, F8, F7, and F4 are not as important as all the previously mentioned features hence received little attention from the model. | [
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"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F3, F8 and F7?"
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] | {'F10': 'mba_p', 'F5': 'specialisation', 'F11': 'etest_p', 'F6': 'gender', 'F9': 'workex', 'F1': 'hsc_s', 'F12': 'hsc_p', 'F2': 'degree_t', 'F3': 'ssc_p', 'F8': 'degree_p', 'F7': 'ssc_b', 'F4': 'hsc_b'} | {'F5': 'F10', 'F12': 'F5', 'F4': 'F11', 'F6': 'F6', 'F11': 'F9', 'F9': 'F1', 'F2': 'F12', 'F10': 'F2', 'F1': 'F3', 'F3': 'F8', 'F7': 'F7', 'F8': 'F4'} | {'C2': 'C1', 'C1': 'C2'} | Placed | {'C1': 'Not Placed', 'C2': 'Placed'} |
SVMClassifier_liner | C2 | Employee Attrition | The most likely label for the given case is C2 since the predicted probability of C1 is only 34.27% and this means that the likelihood of C2 is 65.73%. The most relevant features that led to the C2 classification verdict are F26, F20, F10, F12, and F8. However, some of the features are deemed irrelevant to the above verdict and these include F7, F11, F21, and F14. Among the relevant features with some degree of impact, seven are shown to drive the model's class assignment towards the C1, while the remaining support the C2 prediction. Notable negative features swinging the prediction towards C1 are F26, F20, and F10, while the notable positive features are F12 and F8. The small uncertainty associated with the prediction decision for the given case could be attributed to the fact that all the three most important features are negative features whose values contradict assigning the label C2. | [
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"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F26 and F20.",
"Summarize the direction of influence of the features (F10, F12, F8 and F27) with moderate impact on the prediction made for this test case.",
"Provide a statement on the features with the least impact on the prediction made for this test case."
] | [
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] | {'F26': 'OverTime', 'F20': 'NumCompaniesWorked', 'F10': 'YearsSinceLastPromotion', 'F12': 'BusinessTravel', 'F8': 'MaritalStatus', 'F27': 'RelationshipSatisfaction', 'F2': 'Department', 'F23': 'Age', 'F15': 'Gender', 'F16': 'JobInvolvement', 'F30': 'JobRole', 'F28': 'PerformanceRating', 'F4': 'EnvironmentSatisfaction', 'F24': 'DailyRate', 'F6': 'YearsAtCompany', 'F9': 'YearsWithCurrManager', 'F25': 'Education', 'F22': 'EducationField', 'F17': 'WorkLifeBalance', 'F3': 'DistanceFromHome', 'F7': 'YearsInCurrentRole', 'F11': 'TrainingTimesLastYear', 'F21': 'TotalWorkingYears', 'F14': 'StockOptionLevel', 'F1': 'PercentSalaryHike', 'F5': 'MonthlyRate', 'F19': 'MonthlyIncome', 'F18': 'JobLevel', 'F29': 'HourlyRate', 'F13': 'JobSatisfaction'} | {'F26': 'F26', 'F8': 'F20', 'F15': 'F10', 'F17': 'F12', 'F25': 'F8', 'F18': 'F27', 'F21': 'F2', 'F1': 'F23', 'F23': 'F15', 'F29': 'F16', 'F24': 'F30', 'F19': 'F28', 'F28': 'F4', 'F2': 'F24', 'F13': 'F6', 'F16': 'F9', 'F27': 'F25', 'F22': 'F22', 'F20': 'F17', 'F3': 'F3', 'F14': 'F7', 'F12': 'F11', 'F11': 'F21', 'F10': 'F14', 'F9': 'F1', 'F7': 'F5', 'F6': 'F19', 'F5': 'F18', 'F4': 'F29', 'F30': 'F13'} | {'C1': 'C2', 'C2': 'C1'} | Stay | {'C2': 'Leave', 'C1': 'Leave'} |
RandomForestClassifier | C2 | Printer Sales | Per the classifier for the given data, the most plausible label is C2. F5, F14, F1, and F26 are the main features pushing for the above-mentioned outcome. F7, F12, F16, F2, F10, and F15, on the other hand, have little contribution to the classifier employed here. F24, F21, F22, and F17 have a moderate contribution to the assignment of C2. The classifier's confidence in the label decision above can be attributed to larger positive attributions of F21, F24, F1, and F14 compared to the negative attributions of F22, F8, F5, F18, F26, and F20. | [
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] | 242 | 2,615 | {'C1': '20.00%', 'C2': '80.00%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F5, F26, F22 and F21) with moderate impact on the prediction made for this test case."
] | [
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SGDClassifier | C1 | Job Change of Data Scientists | The least probable class, according to the classification algorithm, is C2, with a prediction probability of 25.12%, therefore, we can conclude that the algorithm is quite confident that the correct label for this data is C1. Analysing the attributions revealed that F3, F4, F10, and F7 are the most relevant features, whereas F2, F11, and F12 are the least relevant features. Increasing the algorithm's response in favour of C1 are the positive features F3, F10, F7, F11, F2, and F5. On the contrary, all the other features, F4, F1, F8, F9, F6, and F12, drive the algorithm towards labelling the given data as C2, hence they are considered negative features. Furthermore, the negative influence on the algorithm is the reason why the confidence level in the C1 is reduced to 74.88%. | [
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"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F5, F6 and F2?"
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] | {'F3': 'city_development_index', 'F10': 'relevent_experience', 'F4': 'city', 'F7': 'major_discipline', 'F1': 'experience', 'F8': 'training_hours', 'F9': 'education_level', 'F5': 'gender', 'F6': 'enrolled_university', 'F2': 'company_type', 'F11': 'last_new_job', 'F12': 'company_size'} | {'F1': 'F3', 'F5': 'F10', 'F3': 'F4', 'F8': 'F7', 'F9': 'F1', 'F2': 'F8', 'F7': 'F9', 'F4': 'F5', 'F6': 'F6', 'F11': 'F2', 'F12': 'F11', 'F10': 'F12'} | {'C1': 'C1', 'C2': 'C2'} | Stay | {'C1': 'Stay', 'C2': 'Leave'} |
SVM_poly | C4 | Mobile Price-Range Classification | According to the classification algorithm, neither C1 nor C2 nor C3 is the correct label for the given case. It is 100.0% certain that C4 is the right label. The higher degree of certainty in the above prediction can be attributed to the positive contributions of F20, F18, and F1. The other positive features include F17, F9, F5, and F19, however, unlike F20, F18, and F1, these features have a moderately low impact on the algorithm's decision. The remaining positive features, F6, F10, F16, and F12, are among the least influential input features considered by the algorithm. There are other features such as F3, F14, F15, and F11 whose contributions only serve to decrease the odds of C4 being the correct label for the given case. Regarding the high confidence of the algorithm with respect to this classification, one can conclude that the negative features have little influence on the algorithm's label decision here. | [
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"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F3, F14 and F15) with moderate impact on the prediction made for this test case."
] | [
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] | {'F20': 'ram', 'F18': 'battery_power', 'F1': 'px_width', 'F3': 'int_memory', 'F14': 'sc_h', 'F15': 'wifi', 'F17': 'fc', 'F11': 'three_g', 'F9': 'mobile_wt', 'F8': 'clock_speed', 'F13': 'm_dep', 'F2': 'n_cores', 'F5': 'pc', 'F7': 'touch_screen', 'F19': 'blue', 'F6': 'talk_time', 'F4': 'sc_w', 'F10': 'px_height', 'F16': 'four_g', 'F12': 'dual_sim'} | {'F11': 'F20', 'F1': 'F18', 'F10': 'F1', 'F4': 'F3', 'F12': 'F14', 'F20': 'F15', 'F3': 'F17', 'F18': 'F11', 'F6': 'F9', 'F2': 'F8', 'F5': 'F13', 'F7': 'F2', 'F8': 'F5', 'F19': 'F7', 'F15': 'F19', 'F14': 'F6', 'F13': 'F4', 'F9': 'F10', 'F17': 'F16', 'F16': 'F12'} | {'C1': 'C1', 'C3': 'C2', 'C2': 'C3', 'C4': 'C4'} | r4 | {'C1': 'r1', 'C2': 'r2', 'C3': 'r3', 'C4': 'r4'} |
DNN | C1 | Ethereum Fraud Detection | The prediction likelihoods across the two classes are 15.35% for class C2 and 84.65% for C1, it can be concluded that C1 is the most probable class label for the given data instance. According to the attribution analysis conducted, the different input variables have varying degrees of influence on the model's decision here. The most influential set of variables is F25, F30, F20, F16, F6, F1, and F10, while the variables with the least influence include F32, F36, F8, F22, F2, and F28. The following or subsequent analysis performed to understand the direction of contribution of of the features will focus on the most influential ones controlling the label selection here. Among the top influential features, F25, F30, F20, F16, and F10, only F25 and F30 have negative contributions, decreasing the probability that C1 is the correct label, and they strongly support labelling the case as C2 instead. Pushing the classification decision in favour of C1 are the positive variables such as F20, F16, and F10. The contributions of the remaining variables, including F6, F1, and F19, have moderate to low influence. All in all, the marginal uncertainty in the decision here is mainly due to the negative influences of F25, F30, F31, and F15, but the positive contributions of F20, F16, F19, F6, F1, and F10 drive the decision higher towards C1. | [
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"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F25, F30, F20, F16 and F10.",
"Summarize the direction of influence of the features (F6, F1 and F19) with moderate impact on the prediction made for this test case.",
"Provide a statement on the features with the least impact on the prediction made for this test case."
] | [
"F25",
"F30",
"F20",
"F16",
"F10",
"F6",
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"F21",
"F32",
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] | {'F25': ' ERC20 uniq rec contract addr', 'F30': ' ERC20 uniq rec token name', 'F20': 'min value received', 'F16': 'Time Diff between first and last (Mins)', 'F10': 'avg val sent', 'F6': ' ERC20 uniq sent token name', 'F1': 'Sent tnx', 'F19': 'Avg min between received tnx', 'F31': 'Unique Received From Addresses', 'F15': ' ERC20 uniq rec addr', 'F13': 'total transactions (including tnx to create contract', 'F11': 'Avg min between sent tnx', 'F12': ' ERC20 uniq sent addr.1', 'F17': 'avg val received', 'F35': 'Unique Sent To Addresses', 'F24': 'max value received ', 'F4': 'max val sent', 'F26': 'min val sent', 'F23': 'Number of Created Contracts', 'F3': 'total ether received', 'F27': ' ERC20 uniq sent addr', 'F34': ' ERC20 total Ether received', 'F37': 'Received Tnx', 'F38': ' ERC20 avg val sent', 'F9': 'total Ether sent', 'F7': ' ERC20 min val sent', 'F29': 'max val sent to contract', 'F14': 'total ether balance', 'F5': ' ERC20 max val sent', 'F33': ' Total ERC20 tnxs', 'F18': ' ERC20 total ether sent', 'F21': ' ERC20 avg val rec', 'F32': 'avg value sent to contract', 'F36': ' ERC20 min val rec', 'F8': ' ERC20 max val rec', 'F22': ' ERC20 total Ether sent contract', 'F2': 'min value sent to contract', 'F28': 'total ether sent contracts'} | {'F30': 'F25', 'F38': 'F30', 'F9': 'F20', 'F3': 'F16', 'F14': 'F10', 'F37': 'F6', 'F4': 'F1', 'F2': 'F19', 'F7': 'F31', 'F28': 'F15', 'F18': 'F13', 'F1': 'F11', 'F29': 'F12', 'F11': 'F17', 'F8': 'F35', 'F10': 'F24', 'F13': 'F4', 'F12': 'F26', 'F6': 'F23', 'F20': 'F3', 'F27': 'F27', 'F24': 'F34', 'F5': 'F37', 'F36': 'F38', 'F19': 'F9', 'F34': 'F7', 'F16': 'F29', 'F22': 'F14', 'F35': 'F5', 'F23': 'F33', 'F25': 'F18', 'F33': 'F21', 'F17': 'F32', 'F31': 'F36', 'F32': 'F8', 'F26': 'F22', 'F15': 'F2', 'F21': 'F28'} | {'C1': 'C2', 'C2': 'C1'} | Fraud | {'C2': 'Not Fraud', 'C1': 'Fraud'} |
KNeighborsClassifier | C2 | Credit Risk Classification | According to the machine learning model, it is more likely that the case's label is C2, with a certainty of 100.0%, and this prediction decision is mainly based on the effects of the following features: F1, F7, F6, F8, and F10 on the model. Apart from F10 and F8, all the other variables mentioned above have a strong positive influence, improving the odds of the prediction class, C2. Together with F10 and F8, the values of variables F11 and F5 indicate that C1 could be the correct label instead. Unlike the top positive variables, F1, F7, and F6, each of these negative variables has a moderate contribution to the final decision. The features F4, F9, F2, and F3 are shown to have made minor contributions to the model's decision in this case. In summary, with only the positive contributions from F1, F7, F6, F2, and F4, the model is very certain of the classification output as indicated by the predicted probabilities across C2 and C1. | [
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"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Summarize the direction of influence of the features (F1, F7, F6 and F10) on the prediction made for this test case.",
"Compare the direction of impact of the features: F8, F11 and F5.",
"Describe the degree of impact of the following features: F4, F9 and F3?"
] | [
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"F7",
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"F8",
"F11",
"F5",
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"F3",
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] | {'F1': 'fea_4', 'F7': 'fea_8', 'F6': 'fea_2', 'F10': 'fea_9', 'F8': 'fea_6', 'F11': 'fea_10', 'F5': 'fea_1', 'F4': 'fea_7', 'F9': 'fea_11', 'F3': 'fea_3', 'F2': 'fea_5'} | {'F4': 'F1', 'F8': 'F7', 'F2': 'F6', 'F9': 'F10', 'F6': 'F8', 'F10': 'F11', 'F1': 'F5', 'F7': 'F4', 'F11': 'F9', 'F3': 'F3', 'F5': 'F2'} | {'C2': 'C2', 'C1': 'C1'} | Low | {'C2': 'Low', 'C1': 'High'} |
SVMClassifier_poly | C2 | Employee Attrition | The classification findings by the model for the case here are as follows: there is a 97.67% chance that C2 is the correct label hence only a marginally low chance of 2.33% that C2 is not the correct label but C1 is. From the above findings, it is valid to conclude that the right class for the given case is C2, and the model is very certain of this decision. The features with the most control and influence on the classification above are F30, F21, F1, F26, and F8 but the influence of the remaining features is either moderate or low or negligible. Some of the features with moderate impact include F28, F29, F22, and F16. Those with low influence are F12, F3, F18, F11, and F5. Finally, those with negligible impact are F20, F24, F19, F27, F14, F17, F7, F25, F9, and F23 since their values are shown to have no impact on the classification made by the model here. The top positive features increasing the prediction likelihood of class C2 are F30, F8, and F13. Conversely, the negative features decreasing the odds in favour of C1 are primarily F21, F22, and F1. | [
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"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F29, F22, F16 and F6?"
] | [
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"F24",
"F19",
"F27",
"F14",
"F17",
"F7",
"F25",
"F9",
"F23"
] | {'F30': 'OverTime', 'F21': 'JobSatisfaction', 'F1': 'BusinessTravel', 'F26': 'MaritalStatus', 'F8': 'EnvironmentSatisfaction', 'F28': 'Department', 'F29': 'Age', 'F22': 'YearsInCurrentRole', 'F16': 'TotalWorkingYears', 'F6': 'WorkLifeBalance', 'F13': 'JobLevel', 'F15': 'JobInvolvement', 'F10': 'EducationField', 'F2': 'JobRole', 'F4': 'MonthlyIncome', 'F12': 'PerformanceRating', 'F3': 'DistanceFromHome', 'F18': 'Education', 'F11': 'Gender', 'F5': 'YearsWithCurrManager', 'F20': 'PercentSalaryHike', 'F24': 'RelationshipSatisfaction', 'F19': 'MonthlyRate', 'F27': 'DailyRate', 'F14': 'YearsSinceLastPromotion', 'F17': 'HourlyRate', 'F7': 'YearsAtCompany', 'F25': 'TrainingTimesLastYear', 'F9': 'StockOptionLevel', 'F23': 'NumCompaniesWorked'} | {'F26': 'F30', 'F30': 'F21', 'F17': 'F1', 'F25': 'F26', 'F28': 'F8', 'F21': 'F28', 'F1': 'F29', 'F14': 'F22', 'F11': 'F16', 'F20': 'F6', 'F5': 'F13', 'F29': 'F15', 'F22': 'F10', 'F24': 'F2', 'F6': 'F4', 'F19': 'F12', 'F3': 'F3', 'F27': 'F18', 'F23': 'F11', 'F16': 'F5', 'F9': 'F20', 'F18': 'F24', 'F7': 'F19', 'F2': 'F27', 'F15': 'F14', 'F4': 'F17', 'F13': 'F7', 'F12': 'F25', 'F10': 'F9', 'F8': 'F23'} | {'C1': 'C2', 'C2': 'C1'} | Stay | {'C2': 'Leave', 'C1': 'Leave'} |
LogisticRegression | C3 | Flight Price-Range Classification | The model is very confident that C3 is the most probable class for the given case, with a probability of 90.48% which means that the other labels are very unlikely. F7 and F3 are the most important variables with respect to this classification verdict while all other variables are shown to have a medium or low impact. Fortunately, the top variables, F7 and F3, have the same direction of influence, increasing the likelihood of C3. Furthermore, while F2 and F1 push the model to predict C3, those pushing for the assignment of a different label are F5, F9, and F4. Finally, many features have a fairly small impact on the final prediction made by the model here, but F8, F9, and F10 have the least impact. | [
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"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F7 (equal to V4) and F3 (equal to V3).",
"Summarize the direction of influence of the features (F1 (equal to V2), F2, F5 (when it is equal to V0) and F12) with moderate impact on the prediction made for this test case.",
"Provide a statement on the features with the least impact on the prediction made for this test case."
] | [
"F7",
"F3",
"F1",
"F2",
"F5",
"F12",
"F6",
"F4",
"F11",
"F8",
"F9",
"F10"
] | {'F7': 'Total_Stops', 'F3': 'Airline', 'F1': 'Destination', 'F2': 'Arrival_hour', 'F5': 'Source', 'F12': 'Duration_hours', 'F6': 'Dep_hour', 'F4': 'Dep_minute', 'F11': 'Arrival_minute', 'F8': 'Journey_month', 'F9': 'Journey_day', 'F10': 'Duration_mins'} | {'F12': 'F7', 'F9': 'F3', 'F11': 'F1', 'F5': 'F2', 'F10': 'F5', 'F7': 'F12', 'F3': 'F6', 'F4': 'F4', 'F6': 'F11', 'F2': 'F8', 'F1': 'F9', 'F8': 'F10'} | {'C3': 'C3', 'C1': 'C2', 'C2': 'C1'} | Low | {'C3': 'Low', 'C2': 'Moderate', 'C1': 'High'} |
SVC | C2 | Water Quality Classification | Despite the reasonably high confidence in the assigned label, the prediction probabilities across the two classes indicate that C1 might be the correct label. F6, F4, F2, and F7 are the factors whose major contributions resulted in the labelling choice mentioned above. According to the analysis, the top two factors, F6 and F4, have a negative influence, leading the classifier to classify the data as C1 rather than C2. F9 is the only other negative variable with a moderate effect when compared to the other two negative variables. Nevertheless, there are several factors, F2, F7, F1, F5, F8, and F3, that favourably support and encourage the classifier to assign C2. All in all, the degree of uncertainty in this classification instance might be explained by just looking at the negative factors' rather strong pull on the classifier towards C1. | [
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"negative",
"negative",
"positive",
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] | 237 | 2,622 | {'C1': '38.68%', 'C2': '61.32%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F5, F8 and F3?"
] | [
"F6",
"F4",
"F2",
"F7",
"F9",
"F1",
"F5",
"F8",
"F3"
] | {'F6': 'Sulfate', 'F4': 'Hardness', 'F2': 'ph', 'F7': 'Conductivity', 'F9': 'Turbidity', 'F1': 'Chloramines', 'F5': 'Solids', 'F8': 'Trihalomethanes', 'F3': 'Organic_carbon'} | {'F5': 'F6', 'F2': 'F4', 'F1': 'F2', 'F6': 'F7', 'F9': 'F9', 'F4': 'F1', 'F3': 'F5', 'F8': 'F8', 'F7': 'F3'} | {'C2': 'C1', 'C1': 'C2'} | Portable | {'C1': 'Not Portable', 'C2': 'Portable'} |
MLPClassifier | C1 | Ethereum Fraud Detection | C2 has a probability estimate of only 6.80%, while that of C1 is 93.20%; consequently, the most likely class for the given case is C1. The important or relevant features considered by the classifier are F16, F1, F22, F30, F4, F5, F21, F17, F27, F8, F11, F10, F36, F35, F6, F34, F28, F7, F38, and F9. Not all input features are relevant when determining the appropriate label and these irrelevant features include F33, F23, and F12. Furthermore, F16 and F1 have a strong positive effect, increasing the odds in favour of C1. In contrast, the F22, F4, and F30 are the negative features, lowering the odds of C1. Comparing the attributions of F16, F5, and F1 features to those of the negative features mentioned above, it is not surprising that the classifier is convinced that C1 is the most likely label here. | [
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] | 243 | 2,613 | {'C2': '6.80%', 'C1': '93.20%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F21, F17, F27 and F8?"
] | [
"F16",
"F1",
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] | {'F16': 'Unique Received From Addresses', 'F1': ' ERC20 total Ether sent contract', 'F22': 'total ether received', 'F30': 'Sent tnx', 'F4': 'Number of Created Contracts', 'F5': ' ERC20 uniq rec token name', 'F21': ' ERC20 uniq rec contract addr', 'F17': 'max value received ', 'F27': 'total transactions (including tnx to create contract', 'F8': ' ERC20 uniq sent addr.1', 'F11': ' ERC20 uniq sent addr', 'F10': 'Received Tnx', 'F36': 'avg val received', 'F35': ' ERC20 uniq rec addr', 'F6': 'avg val sent', 'F34': 'min value received', 'F28': 'Unique Sent To Addresses', 'F7': ' ERC20 uniq sent token name', 'F38': 'Avg min between received tnx', 'F9': 'Time Diff between first and last (Mins)', 'F33': ' ERC20 min val rec', 'F12': ' ERC20 max val rec', 'F23': ' ERC20 min val sent', 'F31': ' ERC20 max val sent', 'F3': ' ERC20 avg val sent', 'F20': ' ERC20 avg val rec', 'F26': ' Total ERC20 tnxs', 'F32': ' ERC20 total ether sent', 'F19': ' ERC20 total Ether received', 'F15': 'total ether balance', 'F24': 'total ether sent contracts', 'F18': 'total Ether sent', 'F14': 'avg value sent to contract', 'F37': 'max val sent to contract', 'F29': 'min value sent to contract', 'F2': 'max val sent', 'F25': 'min val sent', 'F13': 'Avg min between sent tnx'} | {'F7': 'F16', 'F26': 'F1', 'F20': 'F22', 'F4': 'F30', 'F6': 'F4', 'F38': 'F5', 'F30': 'F21', 'F10': 'F17', 'F18': 'F27', 'F29': 'F8', 'F27': 'F11', 'F5': 'F10', 'F11': 'F36', 'F28': 'F35', 'F14': 'F6', 'F9': 'F34', 'F8': 'F28', 'F37': 'F7', 'F2': 'F38', 'F3': 'F9', 'F31': 'F33', 'F32': 'F12', 'F34': 'F23', 'F35': 'F31', 'F36': 'F3', 'F33': 'F20', 'F23': 'F26', 'F25': 'F32', 'F24': 'F19', 'F22': 'F15', 'F21': 'F24', 'F19': 'F18', 'F17': 'F14', 'F16': 'F37', 'F15': 'F29', 'F13': 'F2', 'F12': 'F25', 'F1': 'F13'} | {'C1': 'C2', 'C2': 'C1'} | Fraud | {'C2': 'Not Fraud', 'C1': 'Fraud'} |
BernoulliNB | C1 | German Credit Evaluation | The model is not 100% convinced that the correct label for the data under consideration is C1 since there is a 26.27% chance that labelling the data as C2 is correct. All the input variables are shown to have some degree of influence on the classification decision, with the most influential variables being F9, F1, and F8, whereas F5 and F6 are the least influential. The impact of F4, F2, F7, and F3 can be considered moderate compared to the F9, F1, and F8. The uncertainty surrounding the above classification can be blamed on the fact that the majority of input variables have values suggesting that C2 could be the appropriate label. The negative features that decrease the prediction likelihood of C1 are F9, F8, F7, and F3. However, given that the prediction probability is about 73.73%, it can be said that the influence of positive features, F1, F4, F2, and F5, is enough to swing the model's verdict in favour of C1. | [
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] | 295 | 2,481 | {'C1': '73.73%', 'C2': '26.27%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F7, F3 and F5) with moderate impact on the prediction made for this test case."
] | [
"F9",
"F1",
"F8",
"F4",
"F2",
"F7",
"F3",
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"F6"
] | {'F9': 'Saving accounts', 'F1': 'Sex', 'F8': 'Housing', 'F4': 'Purpose', 'F2': 'Checking account', 'F7': 'Job', 'F3': 'Duration', 'F5': 'Age', 'F6': 'Credit amount'} | {'F5': 'F9', 'F2': 'F1', 'F4': 'F8', 'F9': 'F4', 'F6': 'F2', 'F3': 'F7', 'F8': 'F3', 'F1': 'F5', 'F7': 'F6'} | {'C2': 'C1', 'C1': 'C2'} | Good Credit | {'C1': 'Good Credit', 'C2': 'Bad Credit'} |
SVMClassifier_poly | C1 | Employee Attrition | The model predicted class C1 with an 81.98% prediction likelihood. F13 had the largest impact, followed by F12, F29, F11, F30, F2, F26, F28, F21, F27, F10, F4, F25, F6, F18, F15, F5, F16, F8, and finally, F14, which had the smallest non-zero impact. F13, the feature with the largest impact, contributed against the direction of the prediction, whereas F12, F29, F11, and F30 all contributed positively towards the prediction. Other features that had a negative influence on the prediction included F26 and F28, whereas F2 had a positive influence on the prediction. F22, F23, F1, and F7 are shown to have close to zero attribution in the model's prediction verdict in the given case. | [
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] | 98 | 2,340 | {'C1': '81.98%', 'C2': '18.02%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F13 (with a value equal to V1), F12 (equal to V3), F29 (with a value equal to V0), F11 (equal to V1) and F30.",
"Summarize the direction of influence of the features (F2 (value equal to V0), F26 (value equal to V2) and F28 (value equal to V3)) with moderate impact on the prediction made for this test case.",
"Provide a statement on the features with the least impact on the prediction made for this test case."
] | [
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"F22",
"F23",
"F1",
"F7",
"F20",
"F17",
"F3",
"F24",
"F19",
"F9"
] | {'F13': 'OverTime', 'F12': 'JobSatisfaction', 'F29': 'MaritalStatus', 'F11': 'Department', 'F30': 'NumCompaniesWorked', 'F2': 'BusinessTravel', 'F26': 'JobRole', 'F28': 'EnvironmentSatisfaction', 'F21': 'YearsInCurrentRole', 'F27': 'JobInvolvement', 'F10': 'WorkLifeBalance', 'F4': 'YearsSinceLastPromotion', 'F25': 'TotalWorkingYears', 'F6': 'JobLevel', 'F18': 'Age', 'F15': 'EducationField', 'F5': 'PerformanceRating', 'F16': 'MonthlyRate', 'F8': 'Education', 'F14': 'MonthlyIncome', 'F22': 'DailyRate', 'F23': 'YearsAtCompany', 'F1': 'RelationshipSatisfaction', 'F7': 'TrainingTimesLastYear', 'F20': 'StockOptionLevel', 'F17': 'Gender', 'F3': 'PercentSalaryHike', 'F24': 'HourlyRate', 'F19': 'DistanceFromHome', 'F9': 'YearsWithCurrManager'} | {'F26': 'F13', 'F30': 'F12', 'F25': 'F29', 'F21': 'F11', 'F8': 'F30', 'F17': 'F2', 'F24': 'F26', 'F28': 'F28', 'F14': 'F21', 'F29': 'F27', 'F20': 'F10', 'F15': 'F4', 'F11': 'F25', 'F5': 'F6', 'F1': 'F18', 'F22': 'F15', 'F19': 'F5', 'F7': 'F16', 'F27': 'F8', 'F6': 'F14', 'F2': 'F22', 'F13': 'F23', 'F18': 'F1', 'F12': 'F7', 'F10': 'F20', 'F23': 'F17', 'F9': 'F3', 'F4': 'F24', 'F3': 'F19', 'F16': 'F9'} | {'C1': 'C1', 'C2': 'C2'} | Stay | {'C1': 'Leave', 'C2': 'Leave'} |
SVC | C2 | German Credit Evaluation | This case's label has a 70.83 percent chance of being C2 and per the predicted likelihoods across the alternative labels, C3 has a 29.71 percent chance of being the correct label, however, the model is certain that C1 is not the true label. The most important variables are F2, F3, F1, and F5, whereas the remaining influential variables are listed in order of the magnitude of their contributions: F6, F7, F4, F8, and F9. Three of the nine variables have values that push towards the prediction of label C3 while the other attributes are referred to as positive since their values inspire the prediction of class C2. F2, F3, and F1 are the three attributes that have a negative influence on the prediction judgement, pushing it away from C2 towards the label C3. Finally, it is essential to highlight that the cumulative effect of positive attributes is greater than that of negative attributes, F1, F3, and F2. | [
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] | [
"positive",
"negative",
"negative",
"negative",
"positive",
"positive",
"positive",
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] | 136 | 2,596 | {'C2': '70.83%', 'C3': '29.17%', 'C1': '0.0%'} | [
"Provide a statement summarizing the prediction made for the test case.",
"For the current test instance, describe the direction of influence of the following features: F5, F2, F3, F1 and F6.",
"Compare and contrast the impact of the following features (F7, F4 and F8) on the model’s prediction of C2.",
"Describe the degree of impact of the following features: F9?"
] | [
"F5",
"F2",
"F3",
"F1",
"F6",
"F7",
"F4",
"F8",
"F9"
] | {'F5': 'Checking account', 'F2': 'Duration', 'F3': 'Housing', 'F1': 'Saving accounts', 'F6': 'Sex', 'F7': 'Age', 'F4': 'Purpose', 'F8': 'Job', 'F9': 'Credit amount'} | {'F6': 'F5', 'F8': 'F2', 'F4': 'F3', 'F5': 'F1', 'F2': 'F6', 'F1': 'F7', 'F9': 'F4', 'F3': 'F8', 'F7': 'F9'} | {'C3': 'C2', 'C1': 'C3', 'C2': 'C1'} | Good Credit | {'C2': 'Good Credit', 'C3': 'Bad Credit', 'C1': 'Other'} |
SVC | C2 | Vehicle Insurance Claims | First of all, the classification decision is solely based on the information or data supplied to the prediction model. According to the model, there is a 61.61% chance that C2 is the true label, and a 38.39% chance that C1 is the true label. Since the predicted probability of C2 is higher than that of C1, it is valid to conclude that C2 is most likely the true label. The main feature responsible for this classification is F33, with a very strong positive influence, driving the model's decision higher towards C2. The next set of relevant features are F21, F24, F1, F27, F13, F4, F30, and F25. Among all the features mentioned above, F21, F1, F27, F4, and F30 have negative contributions that are responsible for the decrease in the probability that C2 is the true label. This implies that the contributions of F24, F13, and F25 combined with that of F33 explain why the model is moderately certain that C2 is the true label. | [
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"negligible",
"negligible",
"negligible",
"negligible"
] | 43 | 2,696 | {'C2': '61.61%', 'C1': '38.39%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F25, F3 and F16 (with a value equal to V2)?"
] | [
"F33",
"F21",
"F24",
"F1",
"F27",
"F13",
"F4",
"F30",
"F25",
"F3",
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"F28",
"F9",
"F15",
"F14",
"F12",
"F18",
"F5",
"F6",
"F26",
"F17"
] | {'F33': 'incident_severity', 'F21': 'insured_hobbies', 'F24': 'authorities_contacted', 'F1': 'insured_education_level', 'F27': 'umbrella_limit', 'F13': 'insured_relationship', 'F4': 'auto_make', 'F30': 'insured_occupation', 'F25': 'capital-gains', 'F3': 'policy_deductable', 'F16': 'policy_state', 'F11': 'auto_year', 'F32': 'insured_sex', 'F8': 'vehicle_claim', 'F22': 'incident_city', 'F19': 'number_of_vehicles_involved', 'F20': 'insured_zip', 'F31': 'injury_claim', 'F7': 'property_claim', 'F23': 'incident_type', 'F29': 'total_claim_amount', 'F2': 'police_report_available', 'F10': 'property_damage', 'F28': 'incident_state', 'F9': 'policy_annual_premium', 'F15': 'incident_hour_of_the_day', 'F14': 'collision_type', 'F12': 'capital-loss', 'F18': 'bodily_injuries', 'F5': 'policy_csl', 'F6': 'witnesses', 'F26': 'age', 'F17': 'months_as_customer'} | {'F27': 'F33', 'F23': 'F21', 'F28': 'F24', 'F21': 'F1', 'F5': 'F27', 'F24': 'F13', 'F33': 'F4', 'F22': 'F30', 'F7': 'F25', 'F3': 'F3', 'F18': 'F16', 'F17': 'F11', 'F20': 'F32', 'F16': 'F8', 'F30': 'F22', 'F10': 'F19', 'F6': 'F20', 'F14': 'F31', 'F15': 'F7', 'F25': 'F23', 'F13': 'F29', 'F32': 'F2', 'F31': 'F10', 'F29': 'F28', 'F4': 'F9', 'F9': 'F15', 'F26': 'F14', 'F8': 'F12', 'F11': 'F18', 'F19': 'F5', 'F12': 'F6', 'F2': 'F26', 'F1': 'F17'} | {'C1': 'C2', 'C2': 'C1'} | Fraud | {'C2': 'Not Fraud', 'C1': 'Fraud'} |
GradientBoostingClassifier | C2 | Paris House Classification | Because the prediction probability of C1 is barely 0.70 percent, the classifier outputs the label C2 with near 100 percent confidence based on the values of the input attributes. The effects of F11, F7, and F16 on the aforementioned classification decision are significant. The values of these features are given greater emphasis by the classifier than the others. F16 is has a negative impact among these top features, pushing the prediction judgement towards the least likely class, C1 whereas on the other hand, F11 and F7 are referred to as positive features since they improve the likelihood of the C2 label rather than the C1 label. Finally, unlike the others, the values of F2, F13, F3, and F8 have only a little influence on the label selection made here. | [
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"negative",
"positive",
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] | 154 | 2,520 | {'C2': '99.30%', 'C1': '0.70%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F7, F15, F14 and F6) on the model’s prediction of C2.",
"Summarize the set of features has little to no impact on the prediction?"
] | [
"F11",
"F16",
"F7",
"F15",
"F14",
"F6",
"F17",
"F5",
"F10",
"F4",
"F12",
"F9",
"F1",
"F2",
"F13",
"F3",
"F8"
] | {'F11': 'isNewBuilt', 'F16': 'hasYard', 'F7': 'hasPool', 'F15': 'hasStormProtector', 'F14': 'made', 'F6': 'hasGuestRoom', 'F17': 'squareMeters', 'F5': 'floors', 'F10': 'cityCode', 'F4': 'basement', 'F12': 'price', 'F9': 'numPrevOwners', 'F1': 'numberOfRooms', 'F2': 'attic', 'F13': 'cityPartRange', 'F3': 'garage', 'F8': 'hasStorageRoom'} | {'F3': 'F11', 'F1': 'F16', 'F2': 'F7', 'F4': 'F15', 'F12': 'F14', 'F16': 'F6', 'F6': 'F17', 'F8': 'F5', 'F9': 'F10', 'F13': 'F4', 'F17': 'F12', 'F11': 'F9', 'F7': 'F1', 'F14': 'F2', 'F10': 'F13', 'F15': 'F3', 'F5': 'F8'} | {'C1': 'C2', 'C2': 'C1'} | Basic | {'C2': 'Basic', 'C1': 'Luxury'} |
SGDClassifier | C3 | Flight Price-Range Classification | The classification algorithm arrived at the prediction output based on the variables or information supplied about the case under consideration. The prediction probabilities across the three-class labels, C1, C3, and C2, respectively, are 28.17%, 50.21%, and 21.62%, making C3 the label assigned by the algorithm, judged based on the prediction probabilities. The attributions analysis suggests that F9, F2, F10, and F11 are the positive features that increase the algorithm's prediction response in favour of C3. On the other hand, F5, F8, F6, F12, F1, F3, F4, and F7 have negative contributions in support of labelling the case as either C1 or C2. Overall, judging by the degree of contributions of the positive features, it is not surprising that the algorithm is moderately certain that neither C1 nor C2 is the most probable label for the case under consideration here. | [
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"positive",
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] | 443 | 2,763 | {'C1': '28.17%', 'C3': '50.21%', 'C2': '21.62%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F6, F12, F1 and F3?"
] | [
"F9",
"F2",
"F10",
"F5",
"F11",
"F8",
"F6",
"F12",
"F1",
"F3",
"F4",
"F7"
] | {'F9': 'Airline', 'F2': 'Total_Stops', 'F10': 'Arrival_minute', 'F5': 'Journey_day', 'F11': 'Dep_hour', 'F8': 'Source', 'F6': 'Dep_minute', 'F12': 'Duration_hours', 'F1': 'Destination', 'F3': 'Journey_month', 'F4': 'Duration_mins', 'F7': 'Arrival_hour'} | {'F9': 'F9', 'F12': 'F2', 'F6': 'F10', 'F1': 'F5', 'F3': 'F11', 'F10': 'F8', 'F4': 'F6', 'F7': 'F12', 'F11': 'F1', 'F2': 'F3', 'F8': 'F4', 'F5': 'F7'} | {'C2': 'C1', 'C3': 'C3', 'C1': 'C2'} | Moderate | {'C1': 'Low', 'C3': 'Moderate', 'C2': 'High'} |
RandomForestClassifier | C2 | Paris House Classification | Judging based on the information provided on the case under consideration, the model outputs that the prediction probability of C1 is only 0.48%, indicating that with about 99.52% certainty, the true label here is C2 and in simple terms, the model is very confident that the true label for the case under consideration is C2. The higher degree of certainty in the above classification can be attributed solely to the positive contributions of influential features F7, F12, and F14. Analysis indicates that all the remaining features such as F16, F2, F9, F11, and F6 have moderate to low contributions towards the prediction conclusions above, whereas F15, F3, F10, and F5 are the least relevant features here. The very marginal decrease in the C2's prediction likelihood could be attributed to the influence of negative features F2, F11, F8, F5, and F3 since their contributions support labelling the case as C1 instead. Moderate positive features further driving the model to label this case as C2 are F16, F9, F17, and F6. | [
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"positive",
"negative",
"positive",
"positive",
"negative",
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] | 441 | 2,501 | {'C2': '99.52%', 'C1': '0.48%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F6, F17 and F13?"
] | [
"F7",
"F12",
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"F10",
"F5"
] | {'F7': 'isNewBuilt', 'F12': 'hasYard', 'F14': 'hasPool', 'F16': 'made', 'F2': 'hasStormProtector', 'F9': 'hasGuestRoom', 'F11': 'squareMeters', 'F6': 'floors', 'F17': 'price', 'F13': 'cityCode', 'F4': 'basement', 'F8': 'numPrevOwners', 'F1': 'cityPartRange', 'F15': 'numberOfRooms', 'F3': 'attic', 'F10': 'garage', 'F5': 'hasStorageRoom'} | {'F3': 'F7', 'F1': 'F12', 'F2': 'F14', 'F12': 'F16', 'F4': 'F2', 'F16': 'F9', 'F6': 'F11', 'F8': 'F6', 'F17': 'F17', 'F9': 'F13', 'F13': 'F4', 'F11': 'F8', 'F10': 'F1', 'F7': 'F15', 'F14': 'F3', 'F15': 'F10', 'F5': 'F5'} | {'C1': 'C2', 'C2': 'C1'} | Basic | {'C2': 'Basic', 'C1': 'Luxury'} |
GradientBoostingClassifier | C2 | Basketball Players Career Length Prediction | Judging based on the values of the variables passed to the model with respect to the case under consideration, the output labelling decision is as follows: there is about an 83.98% chance that C2 is the correct label, whereas the likelihood of C1 is only 16.02%, hence the label choice with a higher confidence level is C2. The top-variables influencing this decision are F9, F19, F12, and F2, while the least important variables are F10, F1, and F8. According to the variable contributions analysis performed, only the input variables F11, F4, F16, and F7 exhibit negative attributions, pushing the prediction decision towards the alternative label, C1. The other variables positively support the C2 prediction, shifting the verdict strongly away from the C1 class. In conclusion, positive variables such as F9, F19, F12, F2, F15, and F5 have a higher joint contribution compared to the negative features, which can explain why the model is certain that C2 is the most probable label. | [
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] | 11 | 2,663 | {'C2': '83.98%', 'C1': '16.02%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F12, F2, F18 and F11) on the model’s prediction of C2.",
"Summarize the set of features has little to no impact on the prediction?"
] | [
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"F3",
"F14",
"F7",
"F13",
"F10",
"F8",
"F1"
] | {'F9': 'GamesPlayed', 'F19': 'OffensiveRebounds', 'F12': 'FreeThrowPercent', 'F2': 'FieldGoalPercent', 'F18': '3PointPercent', 'F11': '3PointAttempt', 'F5': 'FieldGoalsMade', 'F15': 'Blocks', 'F6': 'DefensiveRebounds', 'F17': 'Turnovers', 'F4': 'Rebounds', 'F16': 'MinutesPlayed', 'F3': 'FreeThrowAttempt', 'F14': 'Assists', 'F7': '3PointMade', 'F13': 'FieldGoalsAttempt', 'F10': 'PointsPerGame', 'F8': 'Steals', 'F1': 'FreeThrowMade'} | {'F1': 'F9', 'F13': 'F19', 'F12': 'F12', 'F6': 'F2', 'F9': 'F18', 'F8': 'F11', 'F4': 'F5', 'F18': 'F15', 'F14': 'F6', 'F19': 'F17', 'F15': 'F4', 'F2': 'F16', 'F11': 'F3', 'F16': 'F14', 'F7': 'F7', 'F5': 'F13', 'F3': 'F10', 'F17': 'F8', 'F10': 'F1'} | {'C2': 'C2', 'C1': 'C1'} | More than 5 | {'C2': 'More than 5', 'C1': 'Less than 5'} |
RandomForestClassifier | C4 | Mobile Price-Range Classification | The model predicts the class label C4 for the given test instance with a likelihood of about 69.23%. However, there is about a 30.77% chance that the true class label is C2, while the others, C1 and C3, have a 0.0% likelihood. The top features contributing to this prediction decision are F3, F18, F13, and F15, whereas the least important are F4, F20, and F1. Among the top features, while F3 and F18 have values that shift the prediction decision towards the C4 class label, the values of F13 and F15 suggest that the true label could likely be C2. For the features with moderate influence on the decision, F7, F11, F10, and F12 have negative contributions, further decreasing the confidence level in the C4 assignment. On the other hand, the moderate positive influences of F6, F2, F14, F8, and F16 drive the decision further towards the C4 label. Considering the attributions of the input features, it is surprising that the confidence level is just 69.23% since the top feature, F3, has the highest contribution among all the input features. Finally, the values of F17, F20, and F1, though shown to be less important when deciding the correct label for the given case, have positive contributions to the prediction with respect to the given case. | [
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"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F2, F7 (value equal to V0) and F8) with moderate impact on the prediction made for this test case."
] | [
"F3",
"F18",
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"F5",
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"F20",
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] | {'F3': 'ram', 'F18': 'touch_screen', 'F13': 'int_memory', 'F15': 'battery_power', 'F6': 'mobile_wt', 'F2': 'sc_w', 'F7': 'four_g', 'F8': 'talk_time', 'F14': 'sc_h', 'F16': 'wifi', 'F11': 'fc', 'F10': 'three_g', 'F12': 'dual_sim', 'F9': 'n_cores', 'F19': 'px_height', 'F5': 'blue', 'F17': 'clock_speed', 'F4': 'px_width', 'F20': 'm_dep', 'F1': 'pc'} | {'F11': 'F3', 'F19': 'F18', 'F4': 'F13', 'F1': 'F15', 'F6': 'F6', 'F13': 'F2', 'F17': 'F7', 'F14': 'F8', 'F12': 'F14', 'F20': 'F16', 'F3': 'F11', 'F18': 'F10', 'F16': 'F12', 'F7': 'F9', 'F9': 'F19', 'F15': 'F5', 'F2': 'F17', 'F10': 'F4', 'F5': 'F20', 'F8': 'F1'} | {'C3': 'C1', 'C4': 'C4', 'C2': 'C2', 'C1': 'C3'} | r2 | {'C1': 'r1', 'C4': 'r2', 'C2': 'r3', 'C3': 'r4'} |
KNeighborsClassifier | C2 | Water Quality Classification | The given case is likely C2 with a confidence level of 87.50% judged based on the values of the input features supplied to the classifier and according to the attributions analysis, F8 and F4 have a high degree of impact. F1, F2, F5, F7, and F3 have a moderate degree of impact while on the contrary F9 and F6 have little impact. Examining further, the values of F8, F4, F1, and F2 all have a positive influence on the classifier supporting the label assignment decision for the given test case. F5 and F3 are also positively supporting features, whereas F7 has a negative influence on the final classification. Finally, F9 and F6 both have very little contributions, though F6 has significantly less than even F9. | [
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"positive",
"positive",
"positive",
"positive",
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"negative",
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] | 51 | 2,315 | {'C2': '87.50%', 'C1': '12.50%'} | [
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Summarize the direction of influence of the features (F8, F4, F1 and F2) on the prediction made for this test case.",
"Compare the direction of impact of the features: F5, F3 and F7.",
"Describe the degree of impact of the following features: F9 and F6?"
] | [
"F8",
"F4",
"F1",
"F2",
"F5",
"F3",
"F7",
"F9",
"F6"
] | {'F8': 'Hardness', 'F4': 'Sulfate', 'F1': 'Solids', 'F2': 'ph', 'F5': 'Organic_carbon', 'F3': 'Conductivity', 'F7': 'Trihalomethanes', 'F9': 'Turbidity', 'F6': 'Chloramines'} | {'F2': 'F8', 'F5': 'F4', 'F3': 'F1', 'F1': 'F2', 'F7': 'F5', 'F6': 'F3', 'F8': 'F7', 'F9': 'F9', 'F4': 'F6'} | {'C2': 'C2', 'C1': 'C1'} | Not Portable | {'C2': 'Not Portable', 'C1': 'Portable'} |
RandomForestClassifier | C2 | Mobile Price-Range Classification | The label for this example is estimated to be C2 among the four possible classes, with a 73.08 percent chance of being true. C1 is the next most likely label, with a probability of roughly 26.92 percent. The above prediction assessment is mostly dependent on the values of the variables F11, F16, F19, F4, and F6. F11 had the greatest influence, followed by F19, F16, F6, and F4. The positive variables F11, F16, F13, and F5 outnumber the negative variables F19, F6, F4, and F1. Twelve of the twenty variables have values that tilt the prediction towards one of the three other probable classifications. As a result, it is not unexpected that the model is not completely certain of the C2 assigned. Given that the chance of C2's being accurate is 73.08 percent, the model appears to be relatively confident in its final judgement for the data instance under review. | [
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] | 130 | 2,601 | {'C2': '73.08%', 'C1': '26.92%', 'C3': '0.00%', 'C4': '0.00%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F5, F1, F10 and F15?"
] | [
"F11",
"F19",
"F16",
"F6",
"F4",
"F13",
"F5",
"F1",
"F10",
"F15",
"F9",
"F8",
"F7",
"F2",
"F20",
"F18",
"F17",
"F14",
"F12",
"F3"
] | {'F11': 'ram', 'F19': 'px_width', 'F16': 'battery_power', 'F6': 'px_height', 'F4': 'n_cores', 'F13': 'dual_sim', 'F5': 'touch_screen', 'F1': 'int_memory', 'F10': 'wifi', 'F15': 'fc', 'F9': 'four_g', 'F8': 'm_dep', 'F7': 'pc', 'F2': 'mobile_wt', 'F20': 'talk_time', 'F18': 'three_g', 'F17': 'sc_h', 'F14': 'sc_w', 'F12': 'blue', 'F3': 'clock_speed'} | {'F11': 'F11', 'F10': 'F19', 'F1': 'F16', 'F9': 'F6', 'F7': 'F4', 'F16': 'F13', 'F19': 'F5', 'F4': 'F1', 'F20': 'F10', 'F3': 'F15', 'F17': 'F9', 'F5': 'F8', 'F8': 'F7', 'F6': 'F2', 'F14': 'F20', 'F18': 'F18', 'F12': 'F17', 'F13': 'F14', 'F15': 'F12', 'F2': 'F3'} | {'C2': 'C2', 'C1': 'C1', 'C4': 'C3', 'C3': 'C4'} | r1 | {'C2': 'r1', 'C1': 'r2', 'C3': 'r3', 'C4': 'r4'} |
BernoulliNB | C1 | Personal Loan Modelling | The model has classified the instance as C1 due to the effects of the following features: F3, F1, F7, and F8. Based on the values of these variables, the likelihood of the C1 label is 65.51 percent. F8 and F7 are the top positively contributing variables, whereas F3 and F1 are the most adversely contributing variables. Unlike F8 and F7, which have greater influences on the model's prediction choice in this situation, F5 and F4 have fairly modest positive influences. Finally, F2, F6, and F9 show negative predictive effects, however, as compared to F3, their attributions are modest. | [
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] | [
"positive",
"positive",
"negative",
"negative",
"positive",
"positive",
"negative",
"negative",
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] | 135 | 2,592 | {'C2': '34.49%', 'C1': '65.51%'} | [
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Summarize the direction of influence of the features (F8, F7 and F3) on the prediction made for this test case.",
"Compare the direction of impact of the features: F1, F5 and F4.",
"Describe the degree of impact of the following features: F2, F6 and F9?"
] | [
"F8",
"F7",
"F3",
"F1",
"F5",
"F4",
"F2",
"F6",
"F9"
] | {'F8': 'CD Account', 'F7': 'Income', 'F3': 'CCAvg', 'F1': 'Securities Account', 'F5': 'Education', 'F4': 'Mortgage', 'F2': 'Age', 'F6': 'Family', 'F9': 'Extra_service'} | {'F8': 'F8', 'F2': 'F7', 'F4': 'F3', 'F7': 'F1', 'F5': 'F5', 'F6': 'F4', 'F1': 'F2', 'F3': 'F6', 'F9': 'F9'} | {'C1': 'C2', 'C2': 'C1'} | Accept | {'C2': 'Reject', 'C1': 'Accept'} |
DecisionTreeClassifier | C1 | Insurance Churn | Considering the predicted likelihoods across the classes, C1 is confidently chosen as the true label since its likelihood is 93.27%, implying that the likelihood of C2 is only about 6.73%. F1 and F11 are the two features with a very strong positive influence, favouring the prediction of class C1. The following features have a moderate effect and are listed in descending order of influence: F7 and F14 have a negative effect, while F15 and F6 have a positive effect on the prediction of C1. Similar to F7 and F14, the features F10 and F13 also negatively affected the prediction decision. Finally, the values of F8, F16, F3, and F5 are the least important to the model decision for this case. | [
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] | 83 | 2,580 | {'C2': '6.73%', 'C1': '93.27%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F6 (equal to V0), F10 and F13) with moderate impact on the prediction made for this test case."
] | [
"F1",
"F11",
"F7",
"F14",
"F15",
"F6",
"F10",
"F13",
"F12",
"F4",
"F2",
"F9",
"F8",
"F16",
"F3",
"F5"
] | {'F1': 'feature15', 'F11': 'feature14', 'F7': 'feature10', 'F14': 'feature11', 'F15': 'feature5', 'F6': 'feature13', 'F10': 'feature4', 'F13': 'feature3', 'F12': 'feature12', 'F4': 'feature1', 'F2': 'feature7', 'F9': 'feature2', 'F8': 'feature6', 'F16': 'feature0', 'F3': 'feature9', 'F5': 'feature8'} | {'F9': 'F1', 'F8': 'F11', 'F4': 'F7', 'F5': 'F14', 'F15': 'F15', 'F7': 'F6', 'F14': 'F10', 'F13': 'F13', 'F6': 'F12', 'F11': 'F4', 'F1': 'F2', 'F12': 'F9', 'F16': 'F8', 'F10': 'F16', 'F3': 'F3', 'F2': 'F5'} | {'C1': 'C2', 'C2': 'C1'} | Leave | {'C2': 'Stay', 'C1': 'Leave'} |
GradientBoostingClassifier | C2 | Basketball Players Career Length Prediction | The classification output is C2, however, the classifier is somewhat unsure about this prediction decision because the corresponding predicted probability is only 55.19%. F13 is by far the most influential feature whereas F5, F7, and F14 have been recognised as having the biggest effect on prediction output here after F13. The combination of F13, F5, F7, F14, and F11 features has resulted in the classification choice being altered from C2 to C1. While F12, F1, and F18 all have a minor influence on the classification, F12 is the only one that has a positive impact on the C2 classification. In this case, many features had lower influence on the prediction, with F2, F10, F6, F4, and F9 having a marginal effect. | [
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] | 88 | 2,564 | {'C1': '44.81%', 'C2': '55.19%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F13, F5, F7, F14 and F11.",
"Summarize the direction of influence of the features (F12, F1 and F18) with moderate impact on the prediction made for this test case.",
"Provide a statement on the features with the least impact on the prediction made for this test case."
] | [
"F13",
"F5",
"F7",
"F14",
"F11",
"F12",
"F1",
"F18",
"F3",
"F19",
"F17",
"F15",
"F8",
"F16",
"F2",
"F10",
"F6",
"F4",
"F9"
] | {'F13': 'GamesPlayed', 'F5': 'OffensiveRebounds', 'F7': 'FieldGoalPercent', 'F14': 'FreeThrowPercent', 'F11': '3PointPercent', 'F12': '3PointAttempt', 'F1': 'FieldGoalsMade', 'F18': 'Blocks', 'F3': 'DefensiveRebounds', 'F19': 'Turnovers', 'F17': 'Rebounds', 'F15': 'MinutesPlayed', 'F8': 'FreeThrowAttempt', 'F16': '3PointMade', 'F2': 'Assists', 'F10': 'PointsPerGame', 'F6': 'FreeThrowMade', 'F4': 'FieldGoalsAttempt', 'F9': 'Steals'} | {'F1': 'F13', 'F13': 'F5', 'F6': 'F7', 'F12': 'F14', 'F9': 'F11', 'F8': 'F12', 'F4': 'F1', 'F18': 'F18', 'F14': 'F3', 'F19': 'F19', 'F15': 'F17', 'F2': 'F15', 'F11': 'F8', 'F7': 'F16', 'F16': 'F2', 'F3': 'F10', 'F10': 'F6', 'F5': 'F4', 'F17': 'F9'} | {'C2': 'C1', 'C1': 'C2'} | Less than 5 | {'C1': 'More than 5', 'C2': 'Less than 5'} |
LogisticRegression | C1 | Customer Churn Modelling | Judging based on the values of the input variables, the classification algorithm labels the case as C1 since its prediction likelihood is equal to 88.69%. The prediction decision is primarily based on the contributions of F1, F5, and F9, however, F8, F2, and F6 are shown to be the least important variables. Regarding the direction of influence of the variables, F1, F9, F10, F8, and F2 are the positive variables that increase the odds of C1 being the correct label. Driving the prediction toward the alternative label, C2, are the variables F5, F4, F3, F7, and F6. Owing to the fact that the most influential variables, F1 and F9, have strong positive attributions, outweighing the contributions of the negative variables, it is not surprising that the algorithm is certain about the decision made. | [
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"positive",
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] | 335 | 2,484 | {'C1': '88.69%', 'C2': '11.31%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F10, F8 and F2?"
] | [
"F1",
"F9",
"F5",
"F4",
"F3",
"F7",
"F10",
"F8",
"F2",
"F6"
] | {'F1': 'IsActiveMember', 'F9': 'NumOfProducts', 'F5': 'Geography', 'F4': 'Gender', 'F3': 'Age', 'F7': 'CreditScore', 'F10': 'EstimatedSalary', 'F8': 'Balance', 'F2': 'Tenure', 'F6': 'HasCrCard'} | {'F9': 'F1', 'F7': 'F9', 'F2': 'F5', 'F3': 'F4', 'F4': 'F3', 'F1': 'F7', 'F10': 'F10', 'F6': 'F8', 'F5': 'F2', 'F8': 'F6'} | {'C2': 'C1', 'C1': 'C2'} | Stay | {'C1': 'Stay', 'C2': 'Leave'} |
BernoulliNB | C2 | Water Quality Classification | The classification algorithm predicts class C2 with a confidence level of 61.55% and this implies that the probability of the alternative label is only 38.45%. In this case, the top features driving the prediction decision are F5, F1, F2, and F7, followed by F4, F6, F3, F8, and finally F9. Based on the inspections performed to understand the direction of influence of the input features, it can be concluded that F5 has the strongest positive contribution, while F2 has the strongest negative contribution and conversely, all the remaining features have moderate contributions. The other positive features are F1, F4, F6, and F8, whereas the remaining negatives are F7, F3, and F9. All things considered, the influence of the negative features indicates that the likelihood of the C1 label is 38.45% while the positive contributions push the prediction higher towards C2 resulting in the 61.55% prediction confidence. | [
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"positive",
"negative",
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"negative"
] | 101 | 2,713 | {'C2': '61.55%', 'C1': '38.45%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F7, F4 and F6) on the model’s prediction of C2.",
"Summarize the set of features has little to no impact on the prediction?"
] | [
"F5",
"F1",
"F2",
"F7",
"F4",
"F6",
"F3",
"F8",
"F9"
] | {'F5': 'Sulfate', 'F1': 'ph', 'F2': 'Trihalomethanes', 'F7': 'Chloramines', 'F4': 'Organic_carbon', 'F6': 'Hardness', 'F3': 'Solids', 'F8': 'Turbidity', 'F9': 'Conductivity'} | {'F5': 'F5', 'F1': 'F1', 'F8': 'F2', 'F4': 'F7', 'F7': 'F4', 'F2': 'F6', 'F3': 'F3', 'F9': 'F8', 'F6': 'F9'} | {'C1': 'C2', 'C2': 'C1'} | Not Portable | {'C2': 'Not Portable', 'C1': 'Portable'} |
RandomForestClassifier | C3 | Flight Price-Range Classification | The classification model's decision about the true label for the case is based on the information provided to it. Among the three labels, C3, C1, and C2, the model shows without a doubt that neither C1 nor C2 is the true label, given that the probability of C3 being the true label is 100.0%. F11, F3, and F4 are the main contributing factors or variables in the final verdict here since their respective influence outranks the remaining variables. In fact, analysis indicates that F2, F8, and F7 are the least influential variables since they receive little emphasis from the model when making the labelling decision here. In between F11, F3, and F4, and F2, F8, F9, and F7, are the variables such as F1, F5, F12, and F10 with moderate influence on the classification decision here. Among the variables passed to the model, only F1, F10, and F2 are shown to have negative contributions, which suggests that perhaps the true label could be either of the remaining labels. However, given the 100.0% predicted likelihood of C3, it is reasonable to deduce that the positive variables, such as F11, F3, F4, F5, F6, and F12, significantly influence the model's judgement towards C3. | [
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"positive",
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"negative",
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] | 436 | 2,499 | {'C3': '100.00%', 'C1': '0.00%', 'C2': '0.00%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F12, F10 and F6) with moderate impact on the prediction made for this test case."
] | [
"F11",
"F3",
"F4",
"F1",
"F5",
"F12",
"F10",
"F6",
"F9",
"F2",
"F8",
"F7"
] | {'F11': 'Duration_hours', 'F3': 'Airline', 'F4': 'Total_Stops', 'F1': 'Journey_day', 'F5': 'Source', 'F12': 'Duration_mins', 'F10': 'Arrival_hour', 'F6': 'Destination', 'F9': 'Arrival_minute', 'F2': 'Dep_minute', 'F8': 'Journey_month', 'F7': 'Dep_hour'} | {'F7': 'F11', 'F9': 'F3', 'F12': 'F4', 'F1': 'F1', 'F10': 'F5', 'F8': 'F12', 'F5': 'F10', 'F11': 'F6', 'F6': 'F9', 'F4': 'F2', 'F2': 'F8', 'F3': 'F7'} | {'C2': 'C3', 'C3': 'C1', 'C1': 'C2'} | Low | {'C3': 'Low', 'C1': 'Moderate', 'C2': 'High'} |
LogisticRegression | C2 | Basketball Players Career Length Prediction | According to the model, C2 is the class with the higher probability, which is equal to 52.57 percent, of being the label for this selected instance or case. Conversely, there is a 47.43 percent chance that C1 is the correct label showing that the model is less certain about the classification verdict in this case. This uncertainty can be linked to the fact that the majority of variables have values that favour assigning C1. The only variables increasing the model's response to prediction C2 are the positive variables namely: F16, F1, F3, F13, F10, F19, and F8. The top negative variables decreasing the likelihood of C2 are F14 and F5 supported by other negative variables, F18, F6, and F4, that further shift the verdict towards C1. | [
"-0.18",
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] | 165 | 2,387 | {'C1': '47.43%', 'C2': '52.57%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F1, F18 and F6) with moderate impact on the prediction made for this test case."
] | [
"F14",
"F16",
"F5",
"F1",
"F18",
"F6",
"F4",
"F3",
"F15",
"F13",
"F10",
"F7",
"F2",
"F12",
"F19",
"F17",
"F11",
"F8",
"F9"
] | {'F14': '3PointMade', 'F16': '3PointAttempt', 'F5': 'FreeThrowMade', 'F1': 'FreeThrowAttempt', 'F18': 'GamesPlayed', 'F6': 'OffensiveRebounds', 'F4': 'FieldGoalsAttempt', 'F3': 'DefensiveRebounds', 'F15': 'Assists', 'F13': 'MinutesPlayed', 'F10': 'FieldGoalsMade', 'F7': 'Blocks', 'F2': 'Rebounds', 'F12': 'FieldGoalPercent', 'F19': 'Steals', 'F17': 'PointsPerGame', 'F11': 'FreeThrowPercent', 'F8': 'Turnovers', 'F9': '3PointPercent'} | {'F7': 'F14', 'F8': 'F16', 'F10': 'F5', 'F11': 'F1', 'F1': 'F18', 'F13': 'F6', 'F5': 'F4', 'F14': 'F3', 'F16': 'F15', 'F2': 'F13', 'F4': 'F10', 'F18': 'F7', 'F15': 'F2', 'F6': 'F12', 'F17': 'F19', 'F3': 'F17', 'F12': 'F11', 'F19': 'F8', 'F9': 'F9'} | {'C2': 'C1', 'C1': 'C2'} | Less than 5 | {'C1': 'More than 5', 'C2': 'Less than 5'} |
RandomForestClassifier | C2 | Printer Sales | According to the predicted likelihoods across the classes, C1 has a 17.0% chance of being the true label for the given data or case, implying that C2 is the most likely label. F19, F9, and F5 are the most important factors that led to the classification judgments above. The remaining factors have a minor or non-existent impact on the classifier. The classifier most likely ignored the values of F8, F21, F12, F16, F13, and F17 when giving a label to this case since their relative degrees of impact are extremely near to zero. F26, F6, F25, F24, F20, and F7 are considered negative factors among the significant factors because their contributions to the choice tend to reduce the chance that C2 is the correct label. These negatives features lend themselves to the case being classified as C1 but the remaining features contribute positively, raising the likelihood of the C2 classification. | [
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"negligible"
] | 240 | 2,618 | {'C2': '83.00%', 'C1': '17.00%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F26, F14 and F10?"
] | [
"F19",
"F9",
"F5",
"F3",
"F15",
"F22",
"F26",
"F14",
"F10",
"F6",
"F25",
"F23",
"F2",
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"F7",
"F1",
"F4",
"F18",
"F8",
"F21",
"F12",
"F16",
"F13",
"F17"
] | {'F19': 'X8', 'F9': 'X24', 'F5': 'X1', 'F3': 'X2', 'F15': 'X10', 'F22': 'X15', 'F26': 'X25', 'F14': 'X23', 'F10': 'X18', 'F6': 'X4', 'F25': 'X7', 'F23': 'X17', 'F2': 'X3', 'F11': 'X22', 'F24': 'X5', 'F20': 'X9', 'F7': 'X12', 'F1': 'X19', 'F4': 'X11', 'F18': 'X16', 'F8': 'X14', 'F21': 'X21', 'F12': 'X20', 'F16': 'X13', 'F13': 'X6', 'F17': 'X26'} | {'F8': 'F19', 'F24': 'F9', 'F1': 'F5', 'F2': 'F3', 'F10': 'F15', 'F15': 'F22', 'F25': 'F26', 'F23': 'F14', 'F18': 'F10', 'F4': 'F6', 'F7': 'F25', 'F17': 'F23', 'F3': 'F2', 'F22': 'F11', 'F5': 'F24', 'F9': 'F20', 'F12': 'F7', 'F19': 'F1', 'F11': 'F4', 'F16': 'F18', 'F14': 'F8', 'F21': 'F21', 'F20': 'F12', 'F13': 'F16', 'F6': 'F13', 'F26': 'F17'} | {'C2': 'C2', 'C1': 'C1'} | Less | {'C2': 'Less', 'C1': 'More'} |
RandomForestClassifier | C2 | Credit Risk Classification | According to the ML model, C2 is the most likely class label, and we can conclude that the model is quite confident about the decision given that the probability of having C1 as the correct label is only 7.0%. For the case under study, analysis indicates that F6, F1, F11, and F10 are essentially the negative set of features that push the forecast higher towards C1 instead of C2, while F7, F4, F8, and F5 increase the odds of the prediction being equal to C2. In general, the most relevant feature is F7, while F9 and F3 are the least relevant features, with marginal influence on the above classification verdict. In summary, given the very strong positive influence of F7 together with the moderate influence of the other positives, F4, F5, and F8, it is not strange that the model chose to label the case as C2 instead of C1. | [
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] | [
"positive",
"negative",
"positive",
"negative",
"positive",
"positive",
"negative",
"negative",
"negative",
"negative",
"negative"
] | 182 | 2,583 | {'C2': '93.00%', 'C1': '7.00%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F2, F3 and F9?"
] | [
"F7",
"F11",
"F4",
"F6",
"F5",
"F8",
"F1",
"F10",
"F2",
"F3",
"F9"
] | {'F7': 'fea_4', 'F11': 'fea_10', 'F4': 'fea_8', 'F6': 'fea_7', 'F5': 'fea_2', 'F8': 'fea_3', 'F1': 'fea_5', 'F10': 'fea_1', 'F2': 'fea_9', 'F3': 'fea_6', 'F9': 'fea_11'} | {'F4': 'F7', 'F10': 'F11', 'F8': 'F4', 'F7': 'F6', 'F2': 'F5', 'F3': 'F8', 'F5': 'F1', 'F1': 'F10', 'F9': 'F2', 'F6': 'F3', 'F11': 'F9'} | {'C1': 'C2', 'C2': 'C1'} | Low | {'C2': 'Low', 'C1': 'High'} |
MLPClassifier | C2 | Annual Income Earnings | Because the confidence level associated with the other class, C1, is just 2.29%, the model predicts that the given example is likely C2 and to be specific, the model is quite certain that the right label for the given case is C2. All the features are shown to have some degree of influence on the decision above, with F9 and F1 being the least relevant features, while F3 and F13 are the top features. From the analysis performed to understand how each feature contributes to the above prediction assertion, only the features F14, F4, F2, F5, F12, and F1, have negative influences, shifting the prediction verdict towards C1. The remaining features all contribute positively, strongly shifting the prediction towards the assigned label which could explain the prediction confidence level associated with label C2. The most positive features are F13, F11, and F3 with stronger push in favour of the output label and they are supported by other positive features such as F10, F8, F6, and F7 have a moderate degree of influence. | [
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] | 201 | 2,412 | {'C1': '2.29%', 'C2': '97.71%'} | [
"Provide a statement summarizing the prediction made for the test case.",
"For the current test instance, describe the direction of influence of the following features: F3, F13, F14, F11 and F4.",
"Compare and contrast the impact of the following features (F6, F7 and F10) on the model’s prediction of C2.",
"Describe the degree of impact of the following features: F8, F5 and F2?"
] | [
"F3",
"F13",
"F14",
"F11",
"F4",
"F6",
"F7",
"F10",
"F8",
"F5",
"F2",
"F12",
"F9",
"F1"
] | {'F3': 'Capital Gain', 'F13': 'Marital Status', 'F14': 'Capital Loss', 'F11': 'Relationship', 'F4': 'Hours per week', 'F6': 'Education', 'F7': 'Country', 'F10': 'Age', 'F8': 'Occupation', 'F5': 'Sex', 'F2': 'Education-Num', 'F12': 'Workclass', 'F9': 'fnlwgt', 'F1': 'Race'} | {'F11': 'F3', 'F6': 'F13', 'F12': 'F14', 'F8': 'F11', 'F13': 'F4', 'F4': 'F6', 'F14': 'F7', 'F1': 'F10', 'F7': 'F8', 'F10': 'F5', 'F5': 'F2', 'F2': 'F12', 'F3': 'F9', 'F9': 'F1'} | {'C1': 'C1', 'C2': 'C2'} | Above 50K | {'C1': 'Under 50K', 'C2': 'Above 50K'} |
KNNClassifier | C2 | Car Acceptability Valuation | The classifier made the prediction here based on the information provided about the case under consideration, and according to the classifier, the prediction probabilities or likelihoods across the labels C2 and C1 are 100.0% and 0.0%, respectively. All the input features are shown to have different degrees of influence on the final decision here by the classifier. The most influential features are F4 and F3, with F6 and F2 ranked as the least contributing factors. The values of F5 and F1 suggest that perhaps the true label could be C1 since they are the negative features. However, considering the confidence in C2, it is valid to conclude that the joint influence or contribution to the classification of the negative features with respect to the given case is outmatched by the joint positive attribution of F4, F3, F6, and F2. | [
"0.34",
"0.33",
"-0.13",
"-0.12",
"0.06",
"0.04"
] | [
"positive",
"positive",
"negative",
"negative",
"positive",
"positive"
] | 435 | 2,758 | {'C2': '100.00%', 'C1': '0.00%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F1, F5, F6 and F2) with moderate impact on the prediction made for this test case."
] | [
"F4",
"F3",
"F1",
"F5",
"F6",
"F2"
] | {'F4': 'persons', 'F3': 'safety', 'F1': 'lug_boot', 'F5': 'buying', 'F6': 'doors', 'F2': 'maint'} | {'F4': 'F4', 'F6': 'F3', 'F5': 'F1', 'F1': 'F5', 'F3': 'F6', 'F2': 'F2'} | {'C1': 'C2', 'C2': 'C1'} | Unacceptable | {'C2': 'Unacceptable', 'C1': 'Acceptable'} |
LogisticRegression | C2 | Real Estate Investment | For the selected case, the model assigns the label C2. The prediction probability distribution across the classes C1 and C2 is 2.40% and 97.60%, respectively. The most important features considered for this prediction are F6, F4, F18, and F13, while on the other hand, the least relevant features with little contributions to the decision based on the analysis are F9, F5, F2, and F14. The top positive features Increasing the likelihood of the prediction being made are F6, F4, and F13. Pushing the prediction towards the alternative class C1, the top negative features are F18, F16, and F10. F15, F12, F20, F3, and F8 are some of the features that have a moderate impact on the classification decision in this case. | [
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] | 159 | 2,382 | {'C1': '2.40%', 'C2': '97.60%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F6, F4 and F18.",
"Summarize the direction of influence of the features (F13, F16 and F10) with moderate impact on the prediction made for this test case.",
"Provide a statement on the features with the least impact on the prediction made for this test case."
] | [
"F6",
"F4",
"F18",
"F13",
"F16",
"F10",
"F15",
"F12",
"F20",
"F8",
"F3",
"F7",
"F11",
"F17",
"F19",
"F1",
"F9",
"F5",
"F2",
"F14"
] | {'F6': 'Feature7', 'F4': 'Feature4', 'F18': 'Feature2', 'F13': 'Feature14', 'F16': 'Feature15', 'F10': 'Feature8', 'F15': 'Feature20', 'F12': 'Feature1', 'F20': 'Feature17', 'F8': 'Feature3', 'F3': 'Feature16', 'F7': 'Feature18', 'F11': 'Feature10', 'F17': 'Feature5', 'F19': 'Feature6', 'F1': 'Feature12', 'F9': 'Feature19', 'F5': 'Feature13', 'F2': 'Feature9', 'F14': 'Feature11'} | {'F11': 'F6', 'F9': 'F4', 'F1': 'F18', 'F17': 'F13', 'F4': 'F16', 'F3': 'F10', 'F20': 'F15', 'F7': 'F12', 'F6': 'F20', 'F8': 'F8', 'F18': 'F3', 'F19': 'F7', 'F13': 'F11', 'F2': 'F17', 'F10': 'F19', 'F15': 'F1', 'F5': 'F9', 'F16': 'F5', 'F12': 'F2', 'F14': 'F14'} | {'C2': 'C1', 'C1': 'C2'} | Invest | {'C1': 'Ignore', 'C2': 'Invest'} |
MLPClassifier | C2 | Vehicle Insurance Claims | The given instance was labelled as C2 by the model based on the values of its features. The model is about 79.64% certain about this prediction decision, hence, there is a slight chance that the label could be C1. Among the different features, the ones with the most impact on the model are F20, F4, F12, F21, and F14. The most negative feature is F20, and it is significantly pushing the narrative toward the prediction of C1. From this, it is foreseeable that there is a chance that the true label could be C1 which is about 20.36%. The influence of F20 and F12 is somewhat counterbalanced by the values of the features F4, F21, and F14. Other attributes that shift the decision in favour of C1 are F15 and F25. F24 shifts the decision further in the direction of C2 and in addition, F16 supports the model's prediction while the values of F26 and F31 of the given test case contradict the model's decision, decreasing the likelihood of C2. Among the features not relevant to this prediction decision for this case are F28, F3, F29, and F22. | [
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"negligible",
"negligible",
"negligible",
"negligible",
"negligible"
] | 78 | 2,324 | {'C2': '79.64%', 'C1': '20.36%'} | [
"Provide a statement summarizing the prediction made for the test case.",
"For the current test instance, describe the direction of influence of the following features: F20 (value equal to V0), F4 (value equal to V15), F12 (value equal to V2), F21 and F14 (equal to V0).",
"Compare and contrast the impact of the following features (F15 (equal to V3), F25 (when it is equal to V2) and F24 (value equal to V2)) on the model’s prediction of C2.",
"Describe the degree of impact of the following features: F16, F26 and F31 (value equal to V1)?"
] | [
"F20",
"F4",
"F12",
"F21",
"F14",
"F15",
"F25",
"F24",
"F16",
"F26",
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"F33",
"F30",
"F1",
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"F18",
"F7",
"F32",
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"F5",
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"F3",
"F22",
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"F10",
"F8",
"F17",
"F23",
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"F11",
"F9",
"F19",
"F27"
] | {'F20': 'incident_severity', 'F4': 'insured_hobbies', 'F12': 'insured_relationship', 'F21': 'umbrella_limit', 'F14': 'insured_education_level', 'F15': 'authorities_contacted', 'F25': 'incident_type', 'F24': 'policy_csl', 'F16': 'number_of_vehicles_involved', 'F26': 'capital-loss', 'F31': 'property_damage', 'F33': 'insured_occupation', 'F30': 'age', 'F1': 'incident_state', 'F13': 'insured_zip', 'F18': 'collision_type', 'F7': 'property_claim', 'F32': 'injury_claim', 'F2': 'capital-gains', 'F5': 'witnesses', 'F28': 'incident_city', 'F3': 'police_report_available', 'F22': 'months_as_customer', 'F29': 'auto_year', 'F10': 'insured_sex', 'F8': 'policy_state', 'F17': 'vehicle_claim', 'F23': 'total_claim_amount', 'F6': 'bodily_injuries', 'F11': 'incident_hour_of_the_day', 'F9': 'policy_annual_premium', 'F19': 'policy_deductable', 'F27': 'auto_make'} | {'F27': 'F20', 'F23': 'F4', 'F24': 'F12', 'F5': 'F21', 'F21': 'F14', 'F28': 'F15', 'F25': 'F25', 'F19': 'F24', 'F10': 'F16', 'F8': 'F26', 'F31': 'F31', 'F22': 'F33', 'F2': 'F30', 'F29': 'F1', 'F6': 'F13', 'F26': 'F18', 'F15': 'F7', 'F14': 'F32', 'F7': 'F2', 'F12': 'F5', 'F30': 'F28', 'F32': 'F3', 'F1': 'F22', 'F17': 'F29', 'F20': 'F10', 'F18': 'F8', 'F16': 'F17', 'F13': 'F23', 'F11': 'F6', 'F9': 'F11', 'F4': 'F9', 'F3': 'F19', 'F33': 'F27'} | {'C1': 'C2', 'C2': 'C1'} | Not Fraud | {'C2': 'Not Fraud', 'C1': 'Fraud'} |
RandomForestClassifier | C2 | Ethereum Fraud Detection | According to the classification algorithm, the best label for the given case is C2, because there is little to no chance that C1 is the correct label. Not all of the features are found to contribute to the label given here. The following significant features are ordered in order of their effect on the algorithm's output: F4, F6, F17, F3, F14, F24, F38, F37, F16, F8, F11, F31, F29, F28, F25, F20, F5, F33, F35, F2. F21, F19, and F30, on the other hand, are unimportant features since they have almost no influence. Among the most influential features F4, F6, F17, F3, and F14, F17 is considered the most negative, dragging the verdict in a different direction, while the others have positive contributions, increasing the possibility that C2 is correct in this case. F38 is recognised as a positive feature with modest effect, whereas F24 and F37 are identified as negative features. Given that the majority of the top five attributes have positive contributions, boosting the likelihood that C2 is the correct label, it is not unexpected that the algorithm is quite confident in the assigned label's accuracy. | [
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"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F4, F6, F17, F3 and F14.",
"Summarize the direction of influence of the features (F24, F38 and F37) with moderate impact on the prediction made for this test case.",
"Provide a statement on the features with the least impact on the prediction made for this test case."
] | [
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BernoulliNB | C1 | Hotel Satisfaction | The classifier labbelled the given case as C1 with a confidence level of 98.89%, implying that the chance of C2 being the correct label is only about 1.11%. The classification output decision is solely based on the information supplied to the classifier about the case under review. We can rank the contributions of the features as follows: F13, F15, F12, F1, F7, F9, F5, F3, F11, F2, F14, F6, F4, F8, and F10. Among the top features, F13 is the only negative feature, increasing the probability of predicting the alternative label, C2. Other top features that are shifting the prediction towards C1 are F15, F12, and F1. Similar to F13, the features F5, F8, and F2 have negative contributions, supporting the generation of C2. By comparing the strong joint positive attribution to the joint negative attribution, it is evident why the classifier is very certain that C1 is the right label for this instance. | [
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"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F1, F7 and F9) on the model’s prediction of C1.",
"Summarize the set of features has little to no impact on the prediction?"
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] | {'F13': 'Type of Travel', 'F15': 'Type Of Booking', 'F12': 'Common Room entertainment', 'F1': 'Stay comfort', 'F7': 'Cleanliness', 'F9': 'Hotel wifi service', 'F5': 'Other service', 'F3': 'Ease of Online booking', 'F11': 'Age', 'F2': 'Checkin\\/Checkout service', 'F14': 'Food and drink', 'F6': 'Departure\\/Arrival convenience', 'F4': 'purpose_of_travel', 'F8': 'Hotel location', 'F10': 'Gender'} | {'F3': 'F13', 'F4': 'F15', 'F12': 'F12', 'F11': 'F1', 'F15': 'F7', 'F6': 'F9', 'F14': 'F5', 'F8': 'F3', 'F5': 'F11', 'F13': 'F2', 'F10': 'F14', 'F7': 'F6', 'F2': 'F4', 'F9': 'F8', 'F1': 'F10'} | {'C2': 'C1', 'C1': 'C2'} | dissatisfied | {'C1': 'dissatisfied', 'C2': 'satisfied'} |
RandomForestClassifier | C2 | Used Cars Price-Range Prediction | The prediction probability associated with class C1 is 10.50%, while that of class C2 is 89.50%, therefore, it can be concluded that C2 is the most probable label for the given case according to the model. All the input features are shown to contribute to the above decision, and the ones with the strongest influence on the classification decision are F9, F6, and F8, but F1, F7, F3, and F10 are shown to be the least relevant features . Finally, the degree of influence of F4, F5, and F2 can be described as moderate. The model's high confidence can be attributed to the strong positive contributions of F6 and F9 which are supported by the contributions of the remaining positive features F4, F1, and F7. Conversely, shifting the prediction in favour of C1, the negative features F8, F5, F3, F2, and F10. | [
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] | 259 | 2,465 | {'C1': '10.50%', 'C2': '89.50%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F2, F1 and F3) with moderate impact on the prediction made for this test case."
] | [
"F6",
"F9",
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"F5",
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] | {'F6': 'Power', 'F9': 'car_age', 'F8': 'Transmission', 'F4': 'Fuel_Type', 'F5': 'Name', 'F2': 'Mileage', 'F1': 'Engine', 'F3': 'Owner_Type', 'F7': 'Kilometers_Driven', 'F10': 'Seats'} | {'F4': 'F6', 'F5': 'F9', 'F8': 'F8', 'F7': 'F4', 'F6': 'F5', 'F2': 'F2', 'F3': 'F1', 'F9': 'F3', 'F1': 'F7', 'F10': 'F10'} | {'C1': 'C1', 'C2': 'C2'} | High | {'C1': 'Low', 'C2': 'High'} |
SVC | C1 | Food Ordering Customer Churn Prediction | The model labels the case as C1 with fairly high confidence equal to 89.73%, whereas the likelihood of C2 is only 10.27%. Analysis shows that only 20 of the 46 input variables contribute to the prediction assertion above. The prediction judgement C1 is mainly based on the variables F11, F35, F22, and F34. F15, F32, F42, F6, F28, and F4 also contribute to the decision, however, their degree of influence is only moderate. According to the direction of influence analysis, F11, F34, F28, and F6 positively support the decision of the model to assign the label C1. However, F35, F32, F4, F22, F15, and F42 reduce the likelihood or chance that C1 is the true label for this particular test instance. The main variables with less influence on the above classification decision are F1, F43, F40, and F33. | [
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] | 173 | 2,515 | {'C1': '89.73%', 'C2': '10.27%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F11 and F35.",
"Summarize the direction of influence of the features (F34, F22, F15 and F32) with moderate impact on the prediction made for this test case.",
"Provide a statement on the features with the least impact on the prediction made for this test case."
] | [
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] | {'F11': 'Ease and convenient', 'F35': 'Unaffordable', 'F34': 'Good Food quality', 'F22': 'Wrong order delivered', 'F15': 'Delay of delivery person picking up food', 'F32': 'Politeness', 'F42': 'Self Cooking', 'F4': 'Late Delivery', 'F6': 'Health Concern', 'F28': 'More Offers and Discount', 'F23': 'Easy Payment option', 'F16': 'Time saving', 'F5': 'Perference(P2)', 'F19': 'Gender', 'F2': 'Good Road Condition', 'F44': 'Google Maps Accuracy', 'F14': 'Good Taste ', 'F24': 'Good Tracking system', 'F10': 'Bad past experience', 'F7': 'Marital Status', 'F1': 'Influence of rating', 'F40': 'Delivery person ability', 'F43': 'Low quantity low time', 'F33': 'Age', 'F37': 'Less Delivery time', 'F31': 'High Quality of package', 'F41': 'Maximum wait time', 'F17': 'Number of calls', 'F29': 'Freshness ', 'F38': 'Temperature', 'F21': 'Residence in busy location', 'F18': 'Long delivery time', 'F20': 'Order Time', 'F36': 'Influence of time', 'F25': 'Order placed by mistake', 'F27': 'Missing item', 'F12': 'Delay of delivery person getting assigned', 'F45': 'Family size', 'F13': 'Unavailability', 'F46': 'Poor Hygiene', 'F30': 'More restaurant choices', 'F3': 'Perference(P1)', 'F9': 'Educational Qualifications', 'F8': 'Monthly Income', 'F26': 'Occupation', 'F39': 'Good Quantity'} | {'F10': 'F11', 'F23': 'F35', 'F15': 'F34', 'F27': 'F22', 'F26': 'F15', 'F42': 'F32', 'F17': 'F42', 'F19': 'F4', 'F18': 'F6', 'F14': 'F28', 'F13': 'F23', 'F11': 'F16', 'F9': 'F5', 'F2': 'F19', 'F35': 'F2', 'F34': 'F44', 'F45': 'F14', 'F16': 'F24', 'F21': 'F10', 'F3': 'F7', 'F38': 'F1', 'F37': 'F40', 'F36': 'F43', 'F1': 'F33', 'F39': 'F37', 'F40': 'F31', 'F32': 'F41', 'F41': 'F17', 'F43': 'F29', 'F44': 'F38', 'F33': 'F21', 'F24': 'F18', 'F31': 'F20', 'F30': 'F36', 'F29': 'F25', 'F28': 'F27', 'F25': 'F12', 'F7': 'F45', 'F22': 'F13', 'F20': 'F46', 'F12': 'F30', 'F8': 'F3', 'F6': 'F9', 'F5': 'F8', 'F4': 'F26', 'F46': 'F39'} | {'C2': 'C1', 'C1': 'C2'} | Return | {'C1': 'Return', 'C2': 'Go Away'} |
MLPClassifier | C1 | Annual Income Earnings | The label predicted for this case is C1 with very high confidence of approximately 97.71% which insinuates that there is a marginal possibility that C2 could be the label. The above classification decision is largely due to the values of F5, F11, F12, and F4. On the other hand, F7 and F2 are less relevant when the model is deciding the correct label for the case here. Digging deeper revealed that each feature either positively or negatively contribute to the prediction made here. Six features contradicted the classification decision, while the remaining ones positively supported the C1 prediction. The negative features driving the prediction towards C2 are F12, F6, F9, F14, F1, and F2 and countering their influence are the top positive features are F5, F11, F3, and F4. | [
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] | 158 | 2,381 | {'C2': '2.29%', 'C1': '97.71%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F5 and F11.",
"Summarize the direction of influence of the features (F12, F4, F6 and F3) with moderate impact on the prediction made for this test case.",
"Provide a statement on the features with the least impact on the prediction made for this test case."
] | [
"F5",
"F11",
"F12",
"F4",
"F6",
"F3",
"F10",
"F8",
"F13",
"F9",
"F14",
"F1",
"F7",
"F2"
] | {'F5': 'Capital Gain', 'F11': 'Marital Status', 'F12': 'Capital Loss', 'F4': 'Relationship', 'F6': 'Hours per week', 'F3': 'Education', 'F10': 'Country', 'F8': 'Age', 'F13': 'Occupation', 'F9': 'Sex', 'F14': 'Education-Num', 'F1': 'Workclass', 'F7': 'fnlwgt', 'F2': 'Race'} | {'F11': 'F5', 'F6': 'F11', 'F12': 'F12', 'F8': 'F4', 'F13': 'F6', 'F4': 'F3', 'F14': 'F10', 'F1': 'F8', 'F7': 'F13', 'F10': 'F9', 'F5': 'F14', 'F2': 'F1', 'F3': 'F7', 'F9': 'F2'} | {'C2': 'C2', 'C1': 'C1'} | Above 50K | {'C2': 'Under 50K', 'C1': 'Above 50K'} |
SVM_linear | C2 | Wine Quality Prediction | The likelihood of C2 being the correct label for the selected case or instance is 67.54% according to the classifier. This means, there is a 32.46% chance that C1 could be the label and the classification assertion above is influenced mainly by the variables F3, F6, F11, and F10. On the contrary, F2, F5, and F9 are deemed less important when deciding the correct label for this given case. Decreasing the likelihood of the predicted label , C2, are the variables F10, F1, F5, and F9, therefore, these negative variables support the alternative class C1. However, the collective or joint attribution of the top positive variables, F6, F3, and F11 is strong enough to tilt the classification in favour of C2. | [
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] | 176 | 2,396 | {'C1': '32.46%', 'C2': '67.54%'} | [
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Summarize the direction of influence of the features (F3, F6, F11 and F10) on the prediction made for this test case.",
"Compare the direction of impact of the features: F7, F1 and F4.",
"Describe the degree of impact of the following features: F8, F2 and F5?"
] | [
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"F6",
"F11",
"F10",
"F7",
"F1",
"F4",
"F8",
"F2",
"F5",
"F9"
] | {'F3': 'residual sugar', 'F6': 'volatile acidity', 'F11': 'alcohol', 'F10': 'fixed acidity', 'F7': 'chlorides', 'F1': 'sulphates', 'F4': 'citric acid', 'F8': 'free sulfur dioxide', 'F2': 'density', 'F5': 'total sulfur dioxide', 'F9': 'pH'} | {'F4': 'F3', 'F2': 'F6', 'F11': 'F11', 'F1': 'F10', 'F5': 'F7', 'F10': 'F1', 'F3': 'F4', 'F6': 'F8', 'F8': 'F2', 'F7': 'F5', 'F9': 'F9'} | {'C1': 'C1', 'C2': 'C2'} | high quality | {'C1': 'low_quality', 'C2': 'high quality'} |
KNeighborsClassifier | C1 | Credit Risk Classification | The confidence level score with respect to each class label suggests that this case should be labelled as C1. Specifically, there is about an 80.0% chance that C1 is the correct label. However, this implies that there is also about a 20.0% chance that it should be C2. The above prediction decision is based predominantly on the influence of the following features: F5, F4, F7, F3, F11, F2, and F1. According to the analysis, the features F5, F4, and F7 have a very strong positive influence, swinging the prediction decision towards C1. In contrast, the value of F3 also suggests the decision should be the alternative class, C2. Similar to F3, the values of F9, F11, and F2 indicate the label could be C2. However, the influence of these features is very small compared to F5, F4, F7, and F3. Finally, the attributes with a moderately low influence on the final prediction decision for this case include F1, F6, F8, and F10. The values of F1 and F10 have a negative attribution, while F6 and F8 have positive attributions. | [
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"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Summarize the direction of influence of the features (F5, F4, F7 and F3) on the prediction made for this test case.",
"Compare the direction of impact of the features: F9, F11 and F2.",
"Describe the degree of impact of the following features: F1, F6 and F10?"
] | [
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] | {'F5': 'fea_4', 'F4': 'fea_8', 'F7': 'fea_2', 'F3': 'fea_9', 'F9': 'fea_6', 'F11': 'fea_10', 'F2': 'fea_1', 'F1': 'fea_11', 'F6': 'fea_7', 'F10': 'fea_3', 'F8': 'fea_5'} | {'F4': 'F5', 'F8': 'F4', 'F2': 'F7', 'F9': 'F3', 'F6': 'F9', 'F10': 'F11', 'F1': 'F2', 'F11': 'F1', 'F7': 'F6', 'F3': 'F10', 'F5': 'F8'} | {'C2': 'C1', 'C1': 'C2'} | Low | {'C1': 'Low', 'C2': 'High'} |
BernoulliNB | C1 | Job Change of Data Scientists | The classification algorithm is pretty confident that the correct label for the data under consideration is C1, wowever, it is noteworthy to consider that C2 has about a 15.13% chance of being the correct label. The predicted probability of each label is assigned based on the influence of features such as F2, F6, F10, and F11. However, the analysis shows that the values of F1, F7, F12, and F8 are less relevant when classifying the data. Only the features F10, F3, F1, F7, F12, and F8 have negative attributions, decreasing the predicted probability of the assigned label and one can say these features are shifting the prediction decision towards the label C2. | [
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"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F5, F4, F1 and F7?"
] | [
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"F5",
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] | {'F2': 'city', 'F6': 'enrolled_university', 'F10': 'relevent_experience', 'F11': 'city_development_index', 'F3': 'experience', 'F9': 'education_level', 'F5': 'major_discipline', 'F4': 'last_new_job', 'F1': 'gender', 'F7': 'company_size', 'F12': 'company_type', 'F8': 'training_hours'} | {'F3': 'F2', 'F6': 'F6', 'F5': 'F10', 'F1': 'F11', 'F9': 'F3', 'F7': 'F9', 'F8': 'F5', 'F12': 'F4', 'F4': 'F1', 'F10': 'F7', 'F11': 'F12', 'F2': 'F8'} | {'C2': 'C2', 'C1': 'C1'} | Leave | {'C2': 'Stay', 'C1': 'Leave'} |
GradientBoostingClassifier | C2 | Printer Sales | The case, despite having features with considerable negative impact, also has numerous and measurable positive features, so the assignment of the label C2 by the model is very likely since the predicted probability is 91.95% which is very higher than that of C1. The F5, F26, and F23 were the most important features driving the model to arrive at the labelling assignment of C2. F4 and F22 have nearly identical positive attributions, while F8 and F9 has negative impacts, swinging the prediction towards a different label. However, the joint positive impact of F4, F5, F23, and F22 stands out over the impact of F26, F10, F9, and F8, favouring the prediction of the C2 model. All things considered, there are more features with a positive impact than those with negative impact; the mean attribution of the positive attributes is much larger which somewhat explains why the confidence level is very high. Above all, it is important to note that the prediction is made with less emphasis on the values of F14, F18, F1, and F3 hence they are practically irrelevant when it comes to labelling this case. | [
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"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F22, F4 and F8) on the model’s prediction of C2.",
"Summarize the set of features has little to no impact on the prediction?"
] | [
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"F24",
"F17",
"F18",
"F25",
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] | {'F5': 'X24', 'F26': 'X8', 'F23': 'X1', 'F10': 'X21', 'F9': 'X4', 'F22': 'X6', 'F4': 'X3', 'F8': 'X22', 'F7': 'X7', 'F6': 'X15', 'F12': 'X20', 'F20': 'X11', 'F2': 'X10', 'F21': 'X19', 'F15': 'X5', 'F11': 'X16', 'F13': 'X23', 'F16': 'X9', 'F24': 'X17', 'F17': 'X18', 'F18': 'X25', 'F25': 'X14', 'F3': 'X2', 'F1': 'X13', 'F19': 'X12', 'F14': 'X26'} | {'F24': 'F5', 'F8': 'F26', 'F1': 'F23', 'F21': 'F10', 'F4': 'F9', 'F6': 'F22', 'F3': 'F4', 'F22': 'F8', 'F7': 'F7', 'F15': 'F6', 'F20': 'F12', 'F11': 'F20', 'F10': 'F2', 'F19': 'F21', 'F5': 'F15', 'F16': 'F11', 'F23': 'F13', 'F9': 'F16', 'F17': 'F24', 'F18': 'F17', 'F25': 'F18', 'F14': 'F25', 'F2': 'F3', 'F13': 'F1', 'F12': 'F19', 'F26': 'F14'} | {'C1': 'C1', 'C2': 'C2'} | More | {'C1': 'Less', 'C2': 'More'} |
LogisticRegression | C1 | Cab Surge Pricing System | The predicted label is C1 given the predictability of C2 is 28.96% and that of C3 is 23.41%. Considering the probabilities of the classes, the model can be described as being moderately confident. The prediction of C1 can be attributed to the varying degree of contributions of the input features. Attribution analysis indicates that F10, F4, and F12 are considered the most influential. Those with moderate influence are F3, F1, F6, F5, F9, and F8, whereas on the contrary, the least influential ones are F11, F2, and F7. The analysis also revealed that not all the features contribute positively to the prediction decision and amongst the input features, the ones with negative attributions decreasing the likelihood of the C1 prediction are F4, F12, F6, F5, and F9 whereas conversely, the top positive features are F10, F3, and F1. | [
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] | 445 | 2,698 | {'C2': '28.96%', 'C3': '23.41%', 'C1': '47.63%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F5, F9, F8 and F11?"
] | [
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"F12",
"F3",
"F1",
"F6",
"F5",
"F9",
"F8",
"F11",
"F2",
"F7"
] | {'F10': 'Type_of_Cab', 'F4': 'Confidence_Life_Style_Index', 'F12': 'Destination_Type', 'F3': 'Trip_Distance', 'F1': 'Cancellation_Last_1Month', 'F6': 'Life_Style_Index', 'F5': 'Customer_Rating', 'F9': 'Var3', 'F8': 'Var1', 'F11': 'Customer_Since_Months', 'F2': 'Var2', 'F7': 'Gender'} | {'F2': 'F10', 'F5': 'F4', 'F6': 'F12', 'F1': 'F3', 'F8': 'F1', 'F4': 'F6', 'F7': 'F5', 'F11': 'F9', 'F9': 'F8', 'F3': 'F11', 'F10': 'F2', 'F12': 'F7'} | {'C3': 'C2', 'C1': 'C3', 'C2': 'C1'} | C3 | {'C2': 'Low', 'C3': 'Medium', 'C1': 'High'} |
RandomForestClassifier | C1 | Company Bankruptcy Prediction | The model outputs a predicted probability of 2.55% for the C2 label and 97.45% for the C1 label. Judging from above, the most probable class is C1. Hence, C1 is the assigned label by the model, with a very high confidence level. The top features contributing to the prediction assessment above are F10, F67, F41, F4, and F62. However, the values of about twenty features are deemed relevant while the remaining are regarded as irrelevant when classifying the given case. These irrelevant features include F20, F57, F69, and F59. Among the relevant features, F41, F37, F16, F28, F92, and F12 are shown to be the only positive features that increase the model's response in favour of the assigned label C1. In contrast, the majority of the relevant features, mainly F10, F67, F4, and F62, have negative contributions, decreasing the odds of the label C1, hence supporting the assignment of C2 to the given case. | [
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] | 209 | 2,418 | {'C2': '2.55%', 'C1': '97.45%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F62, F3 and F15) on the model’s prediction of C1.",
"Summarize the set of features has little to no impact on the prediction?"
] | [
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] | {'F10': " Net Income to Stockholder's Equity", 'F67': ' Total income\\/Total expense', 'F41': ' Borrowing dependency', 'F4': ' Continuous interest rate (after tax)', 'F62': ' Net Value Per Share (B)', 'F3': ' Cash\\/Current Liability', 'F15': ' Net worth\\/Assets', 'F37': ' Fixed Assets Turnover Frequency', 'F16': ' Interest-bearing debt interest rate', 'F28': ' No-credit Interval', 'F64': ' Net Value Per Share (A)', 'F1': ' Long-term fund suitability ratio (A)', 'F92': ' Equity to Long-term Liability', 'F32': ' Realized Sales Gross Margin', 'F12': ' Current Asset Turnover Rate', 'F53': ' Working Capital to Total Assets', 'F6': ' Interest Coverage Ratio (Interest expense to EBIT)', 'F66': ' Working capitcal Turnover Rate', 'F13': ' Inventory Turnover Rate (times)', 'F86': ' After-tax net Interest Rate', 'F20': ' Working Capital\\/Equity', 'F57': ' Liability to Equity', 'F69': ' Operating Gross Margin', 'F59': ' Cash Flow Per Share', 'F63': ' Contingent liabilities\\/Net worth', 'F44': ' Operating Profit Per Share (Yuan ¥)', 'F27': ' Operating Profit Rate', 'F9': ' Net Worth Turnover Rate (times)', 'F34': ' Continuous Net Profit Growth Rate', 'F74': ' Long-term Liability to Current Assets', 'F38': ' Fixed Assets to Assets', 'F19': ' Inventory and accounts receivable\\/Net value', 'F30': ' Regular Net Profit Growth Rate', 'F81': ' Current Liability to Equity', 'F49': ' Equity to Liability', 'F91': ' Current Liability to Liability', 'F88': ' Operating profit\\/Paid-in capital', 'F11': ' Net Value Per Share (C)', 'F83': ' Operating Funds to Liability', 'F24': ' Current Liability to Current Assets', 'F55': ' Current Ratio', 'F47': ' Quick Assets\\/Current Liability', 'F18': ' Tax rate (A)', 'F36': ' After-tax Net Profit Growth Rate', 'F77': ' Per Share Net profit before tax (Yuan ¥)', 'F8': ' Total Asset Turnover', 'F22': ' CFO to Assets', 'F87': ' Cash Reinvestment %', 'F5': ' Net profit before tax\\/Paid-in capital', 'F31': ' Cash Flow to Equity', 'F25': ' Debt ratio %', 'F2': ' Current Liabilities\\/Liability', 'F71': ' Interest Expense Ratio', 'F76': ' Cash Flow to Sales', 'F52': ' Total Asset Growth Rate', 'F45': ' Inventory\\/Current Liability', 'F73': ' Allocation rate per person', 'F58': ' Operating Expense Rate', 'F39': ' Operating profit per person', 'F80': ' Net Income to Total Assets', 'F84': ' Net Value Growth Rate', 'F14': ' ROA(B) before interest and depreciation after tax', 'F51': ' Cash Flow to Liability', 'F17': ' Inventory\\/Working Capital', 'F21': ' Retained Earnings to Total Assets', 'F7': ' Total assets to GNP price', 'F90': ' Persistent EPS in the Last Four Seasons', 'F26': ' Total debt\\/Total net worth', 'F46': ' Quick Ratio', 'F89': ' Revenue per person', 'F23': ' Non-industry income and expenditure\\/revenue', 'F54': ' Cash\\/Total Assets', 'F42': ' ROA(A) before interest and % after tax', 'F82': ' ROA(C) before interest and depreciation before interest', 'F72': ' Research and development expense rate', 'F48': ' Cash Flow to Total Assets', 'F85': ' Pre-tax net Interest Rate', 'F43': ' Accounts Receivable Turnover', 'F33': ' Current Liability to Assets', 'F75': ' Quick Assets\\/Total Assets', 'F65': ' Total expense\\/Assets', 'F93': ' Operating Profit Growth Rate', 'F50': ' Average Collection Days', 'F40': ' Current Assets\\/Total Assets', 'F29': ' Current Liabilities\\/Equity', 'F35': ' Realized Sales Gross Profit Growth Rate', 'F60': ' Cash flow rate', 'F78': ' Total Asset Return Growth Rate Ratio', 'F70': ' Degree of Financial Leverage (DFL)', 'F68': ' Cash Turnover Rate', 'F79': ' Quick Asset Turnover Rate', 'F61': ' Revenue Per Share (Yuan ¥)', 'F56': ' Gross Profit to Sales'} | {'F59': 'F10', 'F57': 'F67', 'F3': 'F41', 'F12': 'F4', 'F27': 'F62', 'F32': 'F3', 'F84': 'F15', 'F22': 'F37', 'F1': 'F16', 'F56': 'F28', 'F42': 'F64', 'F52': 'F1', 'F23': 'F92', 'F83': 'F32', 'F61': 'F12', 'F67': 'F53', 'F60': 'F6', 'F73': 'F66', 'F18': 'F13', 'F79': 'F86', 'F68': 'F20', 'F66': 'F57', 'F62': 'F69', 'F65': 'F59', 'F64': 'F63', 'F63': 'F44', 'F58': 'F27', 'F55': 'F9', 'F54': 'F34', 'F69': 'F74', 'F74': 'F38', 'F70': 'F19', 'F85': 'F30', 'F92': 'F81', 'F91': 'F49', 'F90': 'F91', 'F89': 'F88', 'F88': 'F11', 'F87': 'F83', 'F86': 'F24', 'F82': 'F55', 'F71': 'F47', 'F81': 'F18', 'F80': 'F36', 'F78': 'F77', 'F77': 'F8', 'F76': 'F22', 'F75': 'F87', 'F72': 'F5', 'F53': 'F31', 'F47': 'F25', 'F51': 'F2', 'F14': 'F71', 'F25': 'F76', 'F24': 'F52', 'F21': 'F45', 'F20': 'F73', 'F19': 'F58', 'F17': 'F39', 'F16': 'F80', 'F15': 'F84', 'F13': 'F14', 'F50': 'F51', 'F11': 'F17', 'F10': 'F21', 'F9': 'F7', 'F8': 'F90', 'F7': 'F26', 'F6': 'F46', 'F5': 'F89', 'F4': 'F23', 'F26': 'F54', 'F28': 'F42', 'F29': 'F82', 'F30': 'F72', 'F49': 'F48', 'F48': 'F85', 'F2': 'F43', 'F46': 'F33', 'F45': 'F75', 'F44': 'F65', 'F43': 'F93', 'F41': 'F50', 'F40': 'F40', 'F39': 'F29', 'F38': 'F35', 'F37': 'F60', 'F36': 'F78', 'F35': 'F70', 'F34': 'F68', 'F33': 'F79', 'F31': 'F61', 'F93': 'F56'} | {'C1': 'C2', 'C2': 'C1'} | Yes | {'C2': 'No', 'C1': 'Yes'} |
SVC | C1 | Broadband Sevice Signup | The algorithm identifies the provided data or case as C1 with a greater level of certainty since the prediction probability of class C2 is just 0.07 percent as a result, C2 is less likely than C1. The influence of input features such as F32, F36, F6, F18, and F33 is mostly responsible for the classification verdict above with only F33 having a negative influence among them, slightly pulling the decision in favour of C2. F32, F36, F6, and F18, on the other hand, make considerable positive contributions in favour of assigning C1 to the data. F31, F41, F2, F16, F22, F27, F29, and F39 are some more features that have a modest effect on the algorithm's decision. But, not all features are demonstrated to influence the classification decision either negatively or positively to the aforementioned classification outcome and in reality, a number of these are demonstrated to be irrelevant for determining the suitable label for this case and these include F10, F25, F28, and F21. All in all, the most important features for this classification instance are F32 and F36, whereas F17 and F12 are the least important. | [
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"Provide a statement summarizing the prediction made for the test case.",
"For the current test instance, describe the direction of influence of the following features: F32 and F36.",
"Compare and contrast the impact of the following features (F6, F18, F33 and F31) on the model’s prediction of C1.",
"Describe the degree of impact of the following features: F41, F29, F16 and F39?"
] | [
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SVM | C1 | Customer Churn Modelling | For the given dataset instance, the label assigned by the classifier is C1 since it has a predicted probability of about 89.16%. On the other hand, there is a 9.0% chance that C3 could be the appropriate label, whereas C2 only has a 1.84% chance of being the true label. The classifier arrived at this classification verdict chiefly due to the influence and contributions of variables such as F4, F3, F9, and F1. However, there is less emphasis on the values of F8, F7, and F6, since their impact on the classifier with respect to the given case is smaller compared to the other variables, hence they are the least ranked features. From the attribution analysis, there are four variables with negative contributions, pushing the verdict in the direction of C3. These negative variables are F4, F1, F2, and F5, and their influence on the classifier could explain why there is a little bit of doubt about the correctness of the C1 class assigned and the notable positive variables are F3, F10, F8, and F9. | [
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] | 12 | 2,299 | {'C1': '89.16%', 'C3': '9.0%', 'C2': '1.84%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F7 and F6 (when it is equal to V1)?"
] | [
"F4",
"F3",
"F9",
"F1",
"F2",
"F10",
"F5",
"F8",
"F7",
"F6"
] | {'F4': 'IsActiveMember', 'F3': 'Age', 'F9': 'Geography', 'F1': 'NumOfProducts', 'F2': 'Gender', 'F10': 'Tenure', 'F5': 'CreditScore', 'F8': 'Balance', 'F7': 'EstimatedSalary', 'F6': 'HasCrCard'} | {'F9': 'F4', 'F4': 'F3', 'F2': 'F9', 'F7': 'F1', 'F3': 'F2', 'F5': 'F10', 'F1': 'F5', 'F6': 'F8', 'F10': 'F7', 'F8': 'F6'} | {'C1': 'C1', 'C2': 'C3', 'C3': 'C2'} | Stay | {'C1': 'Stay', 'C3': 'Leave', 'C2': 'Other'} |
GradientBoostingClassifier | C2 | Basketball Players Career Length Prediction | The case is labelled as C2 by the model but looking at the predicted probabilities across the different classes, there is a 33.63% chance that the label could be C1. To explain the above prediction conclusion, the analysis revealed that the majority of the features have negative influences or attributions, pushing the prediction away from C2 in favour of C1. The negative features include F14, F13, F6, F18, and F5 and the values of these features are ranked higher than any of the positive features. Shifting the prediction in the direction of C2 are the positive features F12, F1, F17, and F11. The analysis also revealed that the values of F3, F16, and F9 are less relevant to the prediction for the case under consideration. | [
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] | 150 | 2,375 | {'C1': '33.63%', 'C2': '66.37%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F14, F13 and F6.",
"Summarize the direction of influence of the features (F18, F5 and F12) with moderate impact on the prediction made for this test case.",
"Provide a statement on the features with the least impact on the prediction made for this test case."
] | [
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"F7",
"F19",
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] | {'F14': 'GamesPlayed', 'F13': 'OffensiveRebounds', 'F6': 'FieldGoalPercent', 'F18': 'FreeThrowPercent', 'F5': '3PointPercent', 'F12': '3PointAttempt', 'F8': 'FieldGoalsMade', 'F10': 'Blocks', 'F4': 'DefensiveRebounds', 'F15': 'Turnovers', 'F1': 'Rebounds', 'F2': 'FreeThrowAttempt', 'F17': 'MinutesPlayed', 'F7': 'Assists', 'F19': 'FieldGoalsAttempt', 'F11': '3PointMade', 'F3': 'PointsPerGame', 'F16': 'FreeThrowMade', 'F9': 'Steals'} | {'F1': 'F14', 'F13': 'F13', 'F6': 'F6', 'F12': 'F18', 'F9': 'F5', 'F8': 'F12', 'F4': 'F8', 'F18': 'F10', 'F14': 'F4', 'F19': 'F15', 'F15': 'F1', 'F11': 'F2', 'F2': 'F17', 'F16': 'F7', 'F5': 'F19', 'F7': 'F11', 'F3': 'F3', 'F10': 'F16', 'F17': 'F9'} | {'C1': 'C1', 'C2': 'C2'} | Less than 5 | {'C1': 'More than 5', 'C2': 'Less than 5'} |
BernoulliNB | C2 | Customer Churn Modelling | The most likely label chosen by the model in this case is C2. The decision above is based on the prediction probabilities for the two possible labels, C2 and C1, which are 94.25% and 5.75%, respectively. The following variables can be ranked from most important to least important based on their contribution to the model when it comes to this instance: F5, F6, F3, F7, F9, F10, F4, F8, F1, and F2. F6 and F5 turned out to be the most important positive variables, supporting the model towards assigning the class C2. The least positive variables are F8 and F4, which have less effect on the model. In fact, most of the input features have negative contributions towards the assignment of class C2, leading to a decision change in favour of the other label, C1. The most negative variables are F9, F3, and F7, and the least negative are F1 and F2. | [
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"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F5, F6, F3, F7 and F9.",
"Summarize the direction of influence of the features (F10, F4 and F8) with moderate impact on the prediction made for this test case.",
"Provide a statement on the features with the least impact on the prediction made for this test case."
] | [
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"F3",
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"F9",
"F10",
"F4",
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] | {'F5': 'IsActiveMember', 'F6': 'NumOfProducts', 'F3': 'Gender', 'F7': 'Geography', 'F9': 'Age', 'F10': 'CreditScore', 'F4': 'EstimatedSalary', 'F8': 'Balance', 'F1': 'HasCrCard', 'F2': 'Tenure'} | {'F9': 'F5', 'F7': 'F6', 'F3': 'F3', 'F2': 'F7', 'F4': 'F9', 'F1': 'F10', 'F10': 'F4', 'F6': 'F8', 'F8': 'F1', 'F5': 'F2'} | {'C1': 'C2', 'C2': 'C1'} | Stay | {'C2': 'Stay', 'C1': 'Leave'} |
BernoulliNB | C1 | Personal Loan Modelling | As per the classification algorithm employed, the most probable label for the data under consideration is C1 since the chances of C2 is very slim and negligible. The main driver behind the labelling decision above is F4. The features with moderate influence are F7, F9, F6, F8, F5, and F1, while those with very small or marginal impact are F2 and F3. The direction of influence of the input features could be used to explain why the algorithm is very confident here. Most of the features have a positive impact, increasing or improving the chances of C1 being the correct label and the feature with a significantly higher contribution, F4, is a positive feature which when coupled with other positives F9, F6, F1, and F5 encourages the prediction or assignment of the C1 label. Furthermore, aside from F7 and F8, the other two negative features, F2 and F3, are shown to have a significantly lower impact on the algorithm and the very marginal doubt in the decision can be attributed to the influence of the negative features. | [
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] | 238 | 2,440 | {'C1': '99.99%', 'C2': '0.01%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F1, F2 and F3?"
] | [
"F4",
"F7",
"F9",
"F6",
"F8",
"F5",
"F1",
"F2",
"F3"
] | {'F4': 'CD Account', 'F7': 'Income', 'F9': 'CCAvg', 'F6': 'Securities Account', 'F8': 'Education', 'F5': 'Family', 'F1': 'Mortgage', 'F2': 'Age', 'F3': 'Extra_service'} | {'F8': 'F4', 'F2': 'F7', 'F4': 'F9', 'F7': 'F6', 'F5': 'F8', 'F3': 'F5', 'F6': 'F1', 'F1': 'F2', 'F9': 'F3'} | {'C2': 'C1', 'C1': 'C2'} | Reject | {'C1': 'Reject', 'C2': 'Accept'} |
MLPClassifier | C1 | Vehicle Insurance Claims | The ML algorithm classifies the provided data or case as C1 with a likelihood of 80.70%, hinting that the likelihood of C2 being the correct label is only 19.30%. This classification decision above is mainly based on the influence or contributions of the input features. The most relevant features driving the classification algorithm to arrive at the above decision are F29, F8, F16, F32, F7, F23, and F25. On the other side, not all of the input features are considered relevant when deciding the appropriate label for the given data instance, and these irrelevant features include F15, F11, F26, F30, and F22. Among the top influential features, F7, F23, and F25 are regarded as negative features since their contributions push the algorithm's decision towards the less likely class, C2, although F29, F8, F16, F18, and F32 have positive contributions, increasing the probability that C1 is the right label here. | [
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] | 28 | 2,680 | {'C2': '19.30%', 'C1': '80.70%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F23, F25 (with a value equal to V7) and F7 (with a value equal to V0)) with moderate impact on the prediction made for this test case."
] | [
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] | {'F29': 'incident_severity', 'F8': 'insured_relationship', 'F32': 'authorities_contacted', 'F16': 'vehicle_claim', 'F23': 'umbrella_limit', 'F25': 'insured_hobbies', 'F7': 'incident_type', 'F18': 'policy_deductable', 'F4': 'auto_make', 'F9': 'number_of_vehicles_involved', 'F20': 'insured_occupation', 'F13': 'property_damage', 'F10': 'incident_state', 'F27': 'auto_year', 'F14': 'capital-loss', 'F5': 'policy_csl', 'F6': 'collision_type', 'F31': 'capital-gains', 'F21': 'property_claim', 'F1': 'incident_hour_of_the_day', 'F15': 'police_report_available', 'F11': 'policy_annual_premium', 'F26': 'incident_city', 'F22': 'insured_zip', 'F30': 'bodily_injuries', 'F19': 'injury_claim', 'F24': 'witnesses', 'F3': 'total_claim_amount', 'F17': 'insured_education_level', 'F2': 'insured_sex', 'F33': 'policy_state', 'F28': 'age', 'F12': 'months_as_customer'} | {'F27': 'F29', 'F24': 'F8', 'F28': 'F32', 'F16': 'F16', 'F5': 'F23', 'F23': 'F25', 'F25': 'F7', 'F3': 'F18', 'F33': 'F4', 'F10': 'F9', 'F22': 'F20', 'F31': 'F13', 'F29': 'F10', 'F17': 'F27', 'F8': 'F14', 'F19': 'F5', 'F26': 'F6', 'F7': 'F31', 'F15': 'F21', 'F9': 'F1', 'F32': 'F15', 'F4': 'F11', 'F30': 'F26', 'F6': 'F22', 'F11': 'F30', 'F14': 'F19', 'F12': 'F24', 'F13': 'F3', 'F21': 'F17', 'F20': 'F2', 'F18': 'F33', 'F2': 'F28', 'F1': 'F12'} | {'C1': 'C2', 'C2': 'C1'} | Fraud | {'C2': 'Not Fraud', 'C1': 'Fraud'} |
SVC | C2 | Broadband Sevice Signup | The predicted probability of class C1 is 12.81% and that of class C2 is 87.19%. Therefore, the label chosen by the model is C2, which is the most probable class. The top two features with significant influence on the prediction verdict above are F41 and F26. These features have positive attributions, shifting the decision higher in support of label C2. Other positive features are F5, F38, F22, and F42. Decreasing the likelihood of the assigned label are the negative features such as F4, F18, F32, and F28. Finally, the values of features such as F30, F3, F36, F34, F20, and F11 are considered irrelevant to the prediction decision above. | [
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"Provide a statement summarizing the prediction made for the test case.",
"For the current test instance, describe the direction of influence of the following features: F41 and F26.",
"Compare and contrast the impact of the following features (F4, F5, F38 and F18) on the model’s prediction of C2.",
"Describe the degree of impact of the following features: F32, F28, F25 and F37?"
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MLPClassifier | C2 | Ethereum Fraud Detection | The C1 has a predicted probability of just 3.10% while that of the C2 is 96.90%, therefore, the most likely class selected by the classifier for the given data is C2. The relevant features contributing to this classification are mainly F3, F5, F21, F19, F24, F10, F18, F33, F32, F36, F4, F16, F13, F35, F6, F27, F31, F34, F12, and F30. As per the attribution analysis, F3 and F5 have a very strong joint positive contribution, increasing the classifier's response higher in favour of C2 than C1. In contrast, F21, F24, and F19 are the top negative features, degrading the classifier's response in favour of C1. Comparing the attributions of F3, F10, and F5 to those of the negative features mentioned above, it is not surprising that the classifier is quite confident that C2 is the most probable label here. | [
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"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F18, F33, F32 and F36?"
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] | {'F3': 'Unique Received From Addresses', 'F5': ' ERC20 total Ether sent contract', 'F21': 'total ether received', 'F19': 'Sent tnx', 'F24': 'Number of Created Contracts', 'F10': ' ERC20 uniq rec token name', 'F18': ' ERC20 uniq rec contract addr', 'F33': 'max value received ', 'F32': 'total transactions (including tnx to create contract', 'F36': ' ERC20 uniq sent addr.1', 'F4': ' ERC20 uniq sent addr', 'F16': 'Received Tnx', 'F13': 'avg val received', 'F35': ' ERC20 uniq rec addr', 'F6': 'avg val sent', 'F27': 'min value received', 'F31': 'Unique Sent To Addresses', 'F34': ' ERC20 uniq sent token name', 'F12': 'Avg min between received tnx', 'F30': 'Time Diff between first and last (Mins)', 'F37': ' ERC20 min val rec', 'F28': ' ERC20 max val rec', 'F15': ' ERC20 min val sent', 'F23': ' ERC20 max val sent', 'F1': ' ERC20 avg val sent', 'F8': ' ERC20 avg val rec', 'F20': ' Total ERC20 tnxs', 'F29': ' ERC20 total ether sent', 'F7': ' ERC20 total Ether received', 'F25': 'total ether balance', 'F17': 'total ether sent contracts', 'F38': 'total Ether sent', 'F2': 'avg value sent to contract', 'F22': 'max val sent to contract', 'F11': 'min value sent to contract', 'F14': 'max val sent', 'F9': 'min val sent', 'F26': 'Avg min between sent tnx'} | {'F7': 'F3', 'F26': 'F5', 'F20': 'F21', 'F4': 'F19', 'F6': 'F24', 'F38': 'F10', 'F30': 'F18', 'F10': 'F33', 'F18': 'F32', 'F29': 'F36', 'F27': 'F4', 'F5': 'F16', 'F11': 'F13', 'F28': 'F35', 'F14': 'F6', 'F9': 'F27', 'F8': 'F31', 'F37': 'F34', 'F2': 'F12', 'F3': 'F30', 'F31': 'F37', 'F32': 'F28', 'F34': 'F15', 'F35': 'F23', 'F36': 'F1', 'F33': 'F8', 'F23': 'F20', 'F25': 'F29', 'F24': 'F7', 'F22': 'F25', 'F21': 'F17', 'F19': 'F38', 'F17': 'F2', 'F16': 'F22', 'F15': 'F11', 'F13': 'F14', 'F12': 'F9', 'F1': 'F26'} | {'C1': 'C1', 'C2': 'C2'} | Fraud | {'C1': 'Not Fraud', 'C2': 'Fraud'} |
RandomForestClassifier | C2 | Printer Sales | There is only a 17.0% chance that C1 is the correct label which implies that the most probable label for the given data or case is C2 given its predicted likelihood of 83.0%. The main influential features resulting in the classification conclusions above are F10, F8, and F18 whereas the remaining features have either a moderate or negligible influence on the classifier. When it comes to assigning a label to this case, the classifier likely ignored the values of F19, F2, F22, F5, F24, and F9 since their respective degrees of influence are very close to zero. Among the influential features, only F1, F15, F23, F12, F13, and F6 are considered negative features mainly due to the fact that their contributions towards the decision here only serve to decrease the likelihood that C2 is the correct label and it can be said that these features favour labelling the case as C1. The remaining features such as F10, F8, F18, F11, F4, F20, and F14, offer positive contributions, increasing the likelihood of the C2 class. | [
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"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F1, F14 and F25?"
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RandomForestClassifier | C1 | Student Job Placement | The classification algorithm's decision on the true label for the given case is solely dependent on the information presented to it. Per the algorithm, the accurate label for the case under consideration is most likely C1, and the 12.47% possibility of C2 reflects only a minor uncertainty in the classification algorithm's certainty. The marginal doubt mentioned above can be blamed on the negative contributions of F5, F11, F3, F2, and F12, supporting the assignment of C2 instead of C1. Conversely, the positive contributions of F7, F6, F10, F1, F9, and F4 are shifting the algorithm's decision higher in favour of label C1, hence the high certainty of its correctness. Overall, F5 and F11 are the most influential negative features, whereas F7 and F6 are the most positive features. Also, F8 is shown to have a negligible influence on the classification decision with respect to the case here. | [
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"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F2, F4 and F12?"
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KNeighborsClassifier | C2 | Printer Sales | The model indicates that the label for this case is likely C2, with an 83.33% chance that it is correct, implying that it is unlikely that C1 is the appropriate class. This predictive assertion is chiefly influenced by the values of the input variables F18, F11, and F24. While the F24 and F11 values positively control the model towards the prediction of C2, the F18 value biases the decision towards C1. However, the combined effect of F24 and F11 outweighs the contribution of F18. In addition, the variables F1, F14, and F7 also positively support the output predictions of the model. F21 has similar direction of contribution that of F18, further decreasing the odds of the C2 label. Unlike all the variables above, F13, F25, F10, F9, F15, and F20 are shown to have very little effect on model predictions with respect to the given case and we can say that their values receive very low consideration from the model. | [
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] | 72 | 2,545 | {'C2': '83.33%', 'C1': '16.67%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F18, F1, F14 and F7) on the model’s prediction of C2.",
"Summarize the set of features has little to no impact on the prediction?"
] | [
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LogisticRegression | C2 | Hotel Satisfaction | The algorithm's forecast for the data instance under consideration is C2, and the decision's confidence level is about 91.36 percent. We can observe from the plot that the variables F6 and F7 are moving the prediction judgement towards the other label, C1. The F13, F9, F8, and F11, on the other hand, have values that have a favourable influence, pushing the data classification choice towards label C2. While F2 and F14 contradict the prediction, F4 and F12 have values that confirm the algorithm's prediction output verdict. | [
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] | 1 | 2,598 | {'C2': '91.36%', 'C1': '8.64%'} | [
"Provide a statement summarizing the prediction made for the test case.",
"For the current test instance, describe the direction of influence of the following features: F6 (value equal to V0) and F7 (with a value equal to V0).",
"Compare and contrast the impact of the following features (F13, F9, F8 and F11) on the model’s prediction of C2.",
"Describe the degree of impact of the following features: F2, F4, F14 and F12?"
] | [
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"F2",
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] | {'F6': 'Type of Travel', 'F7': 'Type Of Booking', 'F13': 'Hotel wifi service', 'F9': 'Common Room entertainment', 'F8': 'Stay comfort', 'F11': 'Other service', 'F2': 'Checkin\\/Checkout service', 'F4': 'Hotel location', 'F14': 'Food and drink', 'F12': 'Cleanliness', 'F15': 'Age', 'F3': 'Departure\\/Arrival convenience', 'F5': 'purpose_of_travel', 'F1': 'Ease of Online booking', 'F10': 'Gender'} | {'F3': 'F6', 'F4': 'F7', 'F6': 'F13', 'F12': 'F9', 'F11': 'F8', 'F14': 'F11', 'F13': 'F2', 'F9': 'F4', 'F10': 'F14', 'F15': 'F12', 'F5': 'F15', 'F7': 'F3', 'F2': 'F5', 'F8': 'F1', 'F1': 'F10'} | {'C2': 'C2', 'C1': 'C1'} | dissatisfied | {'C2': 'dissatisfied', 'C1': 'satisfied'} |
SVC | C1 | Vehicle Insurance Claims | The model classifies this case as C1 and it is noteworthy that there is, however, a 38.26% chance that the true label could be class C2. The uncertainty associated with the classification decision above is higher than expected, which could be attributed to the values of the different input features. The most influential feature is F23, which has a positive effect on the class C1 prediction by the model here. All other features are much less influential, with contributions from F3, F25, F19, and F6 shifting the prediction towards C2. Supporting the model in assigning the label choice, F33 is the next most influential feature. The impacts of the F7 and F5 are moderate, ranking seventh and eighth, respectively. Unfortunately, values of features such as F17, F13, F14, and F28 do not matter when determining the correct label in this instance. | [
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] | 81 | 2,543 | {'C1': '61.74%', 'C2': '38.26%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F7 (when it is equal to V0), F5 (with a value equal to V2) and F30?"
] | [
"F23",
"F3",
"F25",
"F19",
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"F5",
"F30",
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"F1",
"F4",
"F10",
"F8",
"F16",
"F21",
"F26",
"F2",
"F29"
] | {'F23': 'incident_severity', 'F3': 'insured_hobbies', 'F25': 'insured_occupation', 'F19': 'umbrella_limit', 'F6': 'policy_csl', 'F33': 'authorities_contacted', 'F7': 'insured_education_level', 'F5': 'collision_type', 'F30': 'months_as_customer', 'F31': 'vehicle_claim', 'F12': 'insured_relationship', 'F22': 'capital-gains', 'F15': 'auto_make', 'F9': 'injury_claim', 'F20': 'incident_city', 'F27': 'insured_sex', 'F11': 'number_of_vehicles_involved', 'F32': 'incident_hour_of_the_day', 'F24': 'age', 'F18': 'property_claim', 'F17': 'policy_annual_premium', 'F13': 'police_report_available', 'F14': 'property_damage', 'F28': 'incident_state', 'F1': 'policy_deductable', 'F4': 'capital-loss', 'F10': 'insured_zip', 'F8': 'incident_type', 'F16': 'bodily_injuries', 'F21': 'witnesses', 'F26': 'policy_state', 'F2': 'total_claim_amount', 'F29': 'auto_year'} | {'F27': 'F23', 'F23': 'F3', 'F22': 'F25', 'F5': 'F19', 'F19': 'F6', 'F28': 'F33', 'F21': 'F7', 'F26': 'F5', 'F1': 'F30', 'F16': 'F31', 'F24': 'F12', 'F7': 'F22', 'F33': 'F15', 'F14': 'F9', 'F30': 'F20', 'F20': 'F27', 'F10': 'F11', 'F9': 'F32', 'F2': 'F24', 'F15': 'F18', 'F4': 'F17', 'F32': 'F13', 'F31': 'F14', 'F29': 'F28', 'F3': 'F1', 'F8': 'F4', 'F6': 'F10', 'F25': 'F8', 'F11': 'F16', 'F12': 'F21', 'F18': 'F26', 'F13': 'F2', 'F17': 'F29'} | {'C1': 'C1', 'C2': 'C2'} | Fraud | {'C1': 'Fraud', 'C2': 'Not Fraud'} |
SVM_linear | C1 | Wine Quality Prediction | The classification or prediction algorithm indicates that the most probable label for the given data is C1 since there is only a 25.47% chance that C2 could be the correct label. The major factors resulting in the above decision are F1, F2, and F5, while the set of features with moderate influence are F10, F6, F7, and F4. The least vital features are shown to be F9, F11, F8, and F3. In conclusion, it is very surprising to see the uncertainty surrounding the classification here given that only F10 and F7 have a negative impact, driving the algorithm to label the data as C2. To be specific, the contributions of F10 and F7 result in a decrease in the likelihood of C1 being the right label, as indicated by the prediction probabilities across the two possible classes but the influence of these negatives are moderated by the major positive features which are F1, F2, and F5. | [
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"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F7, F4 and F9) with moderate impact on the prediction made for this test case."
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"F7",
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] | {'F1': 'sulphates', 'F2': 'volatile acidity', 'F5': 'total sulfur dioxide', 'F10': 'residual sugar', 'F6': 'alcohol', 'F7': 'free sulfur dioxide', 'F4': 'chlorides', 'F9': 'fixed acidity', 'F11': 'citric acid', 'F8': 'pH', 'F3': 'density'} | {'F10': 'F1', 'F2': 'F2', 'F7': 'F5', 'F4': 'F10', 'F11': 'F6', 'F6': 'F7', 'F5': 'F4', 'F1': 'F9', 'F3': 'F11', 'F9': 'F8', 'F8': 'F3'} | {'C2': 'C2', 'C1': 'C1'} | high quality | {'C2': 'low_quality', 'C1': 'high quality'} |
RandomForestClassifier | C3 | Flight Price-Range Classification | The classification verdict is as follows: the most probable label for this case is C3, and the classifier is certain that neither C1 nor C2 is the correct label. The main drivers for the above classification are F12, F6, and F10, all of which have a strong positive influence, pushing the classifier to choose C3. Other positive features pushing the classification further higher towards C3 include F9, F7, F1, and F2. Not all the input features support the assigned label and the negative features F5, F11, and F8 indicate that the most probable class for this case could different from the assigned label. However, considering the confidence level in the above classification, it is valid to conclude that the classifier paid little attention to the negative features, hence selecting class C3. | [
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] | 250 | 2,456 | {'C3': '100.00%', 'C1': '0.00%', 'C2': '0.00%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F1, F5 and F8?"
] | [
"F12",
"F6",
"F10",
"F9",
"F11",
"F7",
"F1",
"F5",
"F8",
"F2",
"F4",
"F3"
] | {'F12': 'Airline', 'F6': 'Duration_hours', 'F10': 'Total_Stops', 'F9': 'Journey_month', 'F11': 'Source', 'F7': 'Destination', 'F1': 'Arrival_hour', 'F5': 'Journey_day', 'F8': 'Dep_minute', 'F2': 'Arrival_minute', 'F4': 'Duration_mins', 'F3': 'Dep_hour'} | {'F9': 'F12', 'F7': 'F6', 'F12': 'F10', 'F2': 'F9', 'F10': 'F11', 'F11': 'F7', 'F5': 'F1', 'F1': 'F5', 'F4': 'F8', 'F6': 'F2', 'F8': 'F4', 'F3': 'F3'} | {'C1': 'C3', 'C2': 'C1', 'C3': 'C2'} | Low | {'C3': 'Low', 'C1': 'Moderate', 'C2': 'High'} |
RandomForestClassifier | C1 | Ethereum Fraud Detection | The best choice of label for the given case is C1 according to the classification algorithm, since there is little to no chance that C2 is the right class. Not all the features are shown to contribute either positively or negatively towards the label assigned here. The influential features can be ranked according to the associated degree of impact on the algorithm's output as follows: F5, F23, F34, F27, F29, F18, F36, F10, F21, F32, F7, F19, F28, F1, F30, F26, F20, F12, F14, F24. On the other hand, the irrelevant features include F17, F13, and F25 since they have close to zero impact. Among the top influential ones, F5, F23, F34, F27, and F29, the input feature F34 is regarded as the most negative, dragging the verdict in a different direction, while the others have positive contributions, improving the likelihood that the choice of C1 is appropriate in this case. The features with moderate influence are F36, F18, F10 where F36 is identified as a positive feature, while F18 and F10 considered negative features. Since a large number of top features have positive contributions that increase the probability that C1 is the right label, it is not surprising that the algorithm is very confident about the correctness of the assigned label. | [
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] | 233 | 2,435 | {'C2': '0.00%', 'C1': '100.00%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F5, F23, F34, F27 and F29.",
"Summarize the direction of influence of the features (F18, F36 and F10) with moderate impact on the prediction made for this test case.",
"Provide a statement on the features with the least impact on the prediction made for this test case."
] | [
"F5",
"F23",
"F34",
"F27",
"F29",
"F18",
"F36",
"F10",
"F21",
"F32",
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"F11",
"F15",
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] | {'F5': ' ERC20 total Ether sent contract', 'F23': ' ERC20 min val rec', 'F34': 'total transactions (including tnx to create contract', 'F27': ' ERC20 max val rec', 'F29': ' Total ERC20 tnxs', 'F18': ' ERC20 uniq rec addr', 'F36': 'min val sent', 'F10': 'Time Diff between first and last (Mins)', 'F21': 'Sent tnx', 'F32': 'Avg min between received tnx', 'F7': 'min value received', 'F19': ' ERC20 total ether sent', 'F28': 'avg val sent', 'F1': 'max val sent', 'F30': 'Avg min between sent tnx', 'F26': 'Received Tnx', 'F20': ' ERC20 uniq sent token name', 'F12': 'Unique Sent To Addresses', 'F14': ' ERC20 uniq rec token name', 'F24': ' ERC20 uniq rec contract addr', 'F17': 'total Ether sent', 'F13': 'Number of Created Contracts', 'F25': ' ERC20 avg val sent', 'F22': ' ERC20 max val sent', 'F8': ' ERC20 min val sent', 'F16': ' ERC20 avg val rec', 'F33': 'Unique Received From Addresses', 'F4': 'max value received ', 'F3': ' ERC20 uniq sent addr.1', 'F6': 'total ether sent contracts', 'F37': 'avg val received', 'F31': ' ERC20 uniq sent addr', 'F11': 'min value sent to contract', 'F15': 'max val sent to contract', 'F2': ' ERC20 total Ether received', 'F38': 'avg value sent to contract', 'F35': 'total ether balance', 'F9': 'total ether received'} | {'F26': 'F5', 'F31': 'F23', 'F18': 'F34', 'F32': 'F27', 'F23': 'F29', 'F28': 'F18', 'F12': 'F36', 'F3': 'F10', 'F4': 'F21', 'F2': 'F32', 'F9': 'F7', 'F25': 'F19', 'F14': 'F28', 'F13': 'F1', 'F1': 'F30', 'F5': 'F26', 'F37': 'F20', 'F8': 'F12', 'F38': 'F14', 'F30': 'F24', 'F19': 'F17', 'F6': 'F13', 'F36': 'F25', 'F35': 'F22', 'F34': 'F8', 'F33': 'F16', 'F7': 'F33', 'F10': 'F4', 'F29': 'F3', 'F21': 'F6', 'F11': 'F37', 'F27': 'F31', 'F15': 'F11', 'F16': 'F15', 'F24': 'F2', 'F17': 'F38', 'F22': 'F35', 'F20': 'F9'} | {'C1': 'C2', 'C2': 'C1'} | Fraud | {'C2': 'Not Fraud', 'C1': 'Fraud'} |
LogisticRegression | C2 | Student Job Placement | For the given case, the prediction decision is as follows: The probability of C1 being the correct label is only 18.57%, the probability of C2 is 81.43% making it the most probable label for the case here. The certainty of the prediction can be attributed to the influence of variables such as F5, F3, F9, F2, and F1. The least relevant variables considered to arrive at the classification verdict are F6, F4, F7, and F8. F10, F12, and F11 have moderate contributions to the classification here. The attribution analysis performed indicates that F2, F1, F12, F11, F4, and F8 are the negative variables, decreasing the likelihood of C2 in favour of labelling the given case as C1. The variables F5, F3, and F9 have the highest positive influence, which increases the odds of label C2 being the correct label. | [
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] | 408 | 2,491 | {'C1': '18.57%', 'C2': '81.43%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F12, F11, F6 and F4?"
] | [
"F5",
"F3",
"F9",
"F2",
"F1",
"F10",
"F12",
"F11",
"F6",
"F4",
"F7",
"F8"
] | {'F5': 'mba_p', 'F3': 'gender', 'F9': 'degree_t', 'F2': 'specialisation', 'F1': 'workex', 'F10': 'hsc_s', 'F12': 'hsc_p', 'F11': 'ssc_p', 'F6': 'ssc_b', 'F4': 'etest_p', 'F7': 'hsc_b', 'F8': 'degree_p'} | {'F5': 'F5', 'F6': 'F3', 'F10': 'F9', 'F12': 'F2', 'F11': 'F1', 'F9': 'F10', 'F2': 'F12', 'F1': 'F11', 'F7': 'F6', 'F4': 'F4', 'F8': 'F7', 'F3': 'F8'} | {'C2': 'C1', 'C1': 'C2'} | Placed | {'C1': 'Not Placed', 'C2': 'Placed'} |
SGDClassifier | C1 | Flight Price-Range Classification | The output decision of the classifier with respect to the given case is: C1 is the most probable label, followed by C3 and C2. To be specific, the predicted likelihood across the classes are as follows: 86.54% for C1, 13.46% for C3, and finally a 0.0% probability with respect to C2. The moderately high classification confidence could largely be due to the impact of certain input features supplied to the classifier. F3, F9, F5, F12, and F8 are the top-ranked variables whereas the least ranked are F1, F6, F10, F4, F2, F11, and F7. The marginal uncertainty in the classification verdict is due to the negative attributions of F9, F8, F10, F2, and F7 which prefer labelling the case differently. In conclusion, we can see that F3, F5, F12, F6, and F1 are among the positive variables pushing the classification in favour of C1. | [
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"positive",
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] | 451 | 2,704 | {'C1': '86.54%', 'C3': '13.46%', 'C2': '0.00%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F5, F12, F8 and F1) with moderate impact on the prediction made for this test case."
] | [
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"F5",
"F12",
"F8",
"F1",
"F6",
"F10",
"F4",
"F2",
"F11",
"F7"
] | {'F3': 'Airline', 'F9': 'Total_Stops', 'F5': 'Source', 'F12': 'Journey_month', 'F8': 'Arrival_minute', 'F1': 'Journey_day', 'F6': 'Duration_hours', 'F10': 'Dep_hour', 'F4': 'Destination', 'F2': 'Arrival_hour', 'F11': 'Dep_minute', 'F7': 'Duration_mins'} | {'F9': 'F3', 'F12': 'F9', 'F10': 'F5', 'F2': 'F12', 'F6': 'F8', 'F1': 'F1', 'F7': 'F6', 'F3': 'F10', 'F11': 'F4', 'F5': 'F2', 'F4': 'F11', 'F8': 'F7'} | {'C1': 'C1', 'C2': 'C3', 'C3': 'C2'} | Low | {'C1': 'Low', 'C3': 'Moderate', 'C2': 'High'} |
LogisticRegression | C2 | Airline Passenger Satisfaction | C2 is the predicted label assigned to this case or instance. This is based on the fact that there is only a 0.68% chance that C1 is the correct label. The most relevant variables that increase the prediction's probability are F17, F21, F8, and F5. Conversely, F16 is the only important feature driving the classification decision in the direction of C1. Other negative features include F10, F3, F9, and F11. Other positive features increasing the chances of the C2 prediction are F15, F19, and F1. Unlike F17, F21, F8, and F5, these positive variables have moderate contributions to the model's decision. The least ranked among all the relevant features are F13, F7, F6, and F12, with lower attributions to the C2 prediction, however, F14 and F4 are shown to have no impact when determining the correct label for the case under consideration. | [
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] | 162 | 2,384 | {'C2': '99.32%', 'C1': '0.68%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F21, F5, F8 and F10) with moderate impact on the prediction made for this test case."
] | [
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] | {'F17': 'Type of Travel', 'F16': 'Customer Type', 'F21': 'Inflight entertainment', 'F5': 'Inflight wifi service', 'F8': 'Departure\\/Arrival time convenient', 'F10': 'Gate location', 'F3': 'Arrival Delay in Minutes', 'F9': 'Seat comfort', 'F11': 'Online boarding', 'F15': 'Ease of Online booking', 'F19': 'Class', 'F1': 'Age', 'F20': 'On-board service', 'F22': 'Cleanliness', 'F2': 'Checkin service', 'F18': 'Inflight service', 'F13': 'Food and drink', 'F7': 'Departure Delay in Minutes', 'F6': 'Baggage handling', 'F12': 'Gender', 'F14': 'Flight Distance', 'F4': 'Leg room service'} | {'F4': 'F17', 'F2': 'F16', 'F14': 'F21', 'F7': 'F5', 'F8': 'F8', 'F10': 'F10', 'F22': 'F3', 'F13': 'F9', 'F12': 'F11', 'F9': 'F15', 'F5': 'F19', 'F3': 'F1', 'F15': 'F20', 'F20': 'F22', 'F18': 'F2', 'F19': 'F18', 'F11': 'F13', 'F21': 'F7', 'F17': 'F6', 'F1': 'F12', 'F6': 'F14', 'F16': 'F4'} | {'C2': 'C2', 'C1': 'C1'} | neutral or dissatisfied | {'C2': 'neutral or dissatisfied', 'C1': 'satisfied'} |
RandomForestClassifier | C1 | Personal Loan Modelling | The following classification decisions are largely based on the factors or attributes of this particular case. The class label, in this case, is projected to be C1 out of the potential classes, which is 97.50% likely. The next possible label is C2, which has an approximate probability of 2.50%. The confidence level with respect to this classification is very high, and the features with the most contributions are F2, F7, and F6. However, F9, F1, and F8 are shown to be the least relevant features. The attribution analysis shows that the only positive features whose contributions favour labelling the case as C1 are F7, F6, F3, and F5. However, the negative attributions of F2, F4, F1, F9, and F8 also indicate that perhaps C2 could be the true label. Judging based on the confidence level coupled with the attributions, it can be concluded that the values of the positive features F7, F6, F3, and F5 are good enough to steer the classification in the direction of C1, but the strong negative attribution of F2 casts about 2.50% of doubt on the decision. | [
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"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F1, F9 and F8?"
] | [
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] | {'F2': 'Income', 'F7': 'CD Account', 'F6': 'Education', 'F4': 'Securities Account', 'F3': 'CCAvg', 'F5': 'Family', 'F1': 'Extra_service', 'F9': 'Age', 'F8': 'Mortgage'} | {'F2': 'F2', 'F8': 'F7', 'F5': 'F6', 'F7': 'F4', 'F4': 'F3', 'F3': 'F5', 'F9': 'F1', 'F1': 'F9', 'F6': 'F8'} | {'C1': 'C1', 'C2': 'C2'} | Reject | {'C1': 'Reject', 'C2': 'Accept'} |
DNN | C1 | Ethereum Fraud Detection | The prediction probabilities for classes C2 and C1, respectively, are 15.35% and 84.65%. Based on the aforementioned, C1 is the most likely class label for the presented data instance, and according to the attribution analysis, the various input variables had varying degrees of impact on the model's classification judgement. F29, F2, F14, F8, F1, F22, and F38 are the most influential factors, whereas F9, F19, F4, F23, F13, and F5 have the least impact. The subsequent analysis will concentrate on the most relevant factors influencing the label selection in this case. Looking at the attributions of the input features, only F29 and F2 exhibit negative contributions among the top influential features, F29, F2, F14, F8, and F38, lowering the chance that C1 is the right label, and they strongly favour labelling the instance as C2 instead. Positive variables such as F14, F8, and F38 influence the classification choice in favour of C1. The remaining variables, including F1, F22, and F25, have a moderate to low impact. In essence, the marginal uncertainty in this decision is mostly owing to the negative impacts of F29, F2, F20, and F3, while the positive contributions of F14, F8, F25, F1, F22, and F38 push the decision much closer to C1. | [
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"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F29, F2, F14, F8 and F38.",
"Summarize the direction of influence of the features (F1, F22 and F25) with moderate impact on the prediction made for this test case.",
"Provide a statement on the features with the least impact on the prediction made for this test case."
] | [
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] | {'F29': ' ERC20 uniq rec contract addr', 'F2': ' ERC20 uniq rec token name', 'F14': 'min value received', 'F8': 'Time Diff between first and last (Mins)', 'F38': 'avg val sent', 'F1': ' ERC20 uniq sent token name', 'F22': 'Sent tnx', 'F25': 'Avg min between received tnx', 'F20': 'Unique Received From Addresses', 'F3': ' ERC20 uniq rec addr', 'F28': 'total transactions (including tnx to create contract', 'F11': 'Avg min between sent tnx', 'F32': ' ERC20 uniq sent addr.1', 'F7': 'avg val received', 'F6': 'Unique Sent To Addresses', 'F10': 'max value received ', 'F33': 'max val sent', 'F18': 'min val sent', 'F34': 'Number of Created Contracts', 'F36': 'total ether received', 'F16': ' ERC20 uniq sent addr', 'F31': ' ERC20 total Ether received', 'F24': 'Received Tnx', 'F12': ' ERC20 avg val sent', 'F17': 'total Ether sent', 'F27': ' ERC20 min val sent', 'F21': 'max val sent to contract', 'F26': 'total ether balance', 'F37': ' ERC20 max val sent', 'F30': ' Total ERC20 tnxs', 'F35': ' ERC20 total ether sent', 'F15': ' ERC20 avg val rec', 'F4': 'avg value sent to contract', 'F19': ' ERC20 min val rec', 'F9': ' ERC20 max val rec', 'F5': ' ERC20 total Ether sent contract', 'F13': 'min value sent to contract', 'F23': 'total ether sent contracts'} | {'F30': 'F29', 'F38': 'F2', 'F9': 'F14', 'F3': 'F8', 'F14': 'F38', 'F37': 'F1', 'F4': 'F22', 'F2': 'F25', 'F7': 'F20', 'F28': 'F3', 'F18': 'F28', 'F1': 'F11', 'F29': 'F32', 'F11': 'F7', 'F8': 'F6', 'F10': 'F10', 'F13': 'F33', 'F12': 'F18', 'F6': 'F34', 'F20': 'F36', 'F27': 'F16', 'F24': 'F31', 'F5': 'F24', 'F36': 'F12', 'F19': 'F17', 'F34': 'F27', 'F16': 'F21', 'F22': 'F26', 'F35': 'F37', 'F23': 'F30', 'F25': 'F35', 'F33': 'F15', 'F17': 'F4', 'F31': 'F19', 'F32': 'F9', 'F26': 'F5', 'F15': 'F13', 'F21': 'F23'} | {'C2': 'C2', 'C1': 'C1'} | Fraud | {'C2': 'Not Fraud', 'C1': 'Fraud'} |
RandomForestClassifier | C2 | Company Bankruptcy Prediction | The model assigns the class C2 with near perfect certainty or confidence level since the predicted likelihood of C1 is only 1.0%. F24, F92, F41, F67, and F56 have the greatest cumulative beneficial influence on the model's choice to create C2. F40 also had a significant influence, but it shifted the choice away from C2. Furthermore, F78 and F21 had a modest influence on C2 decision making, which was still bigger than features F60 and F2, which had a moderate impact and contributed to C1 class prediction. Furthermore, F21, F71, and F9 have minimal positive impact on the final result, further increasing the chances of C2 being the appropriate label for the given case. However, a number of input features, notably F59, F90, F68, and F76, appear to be less essential to predictions here. All in all, the very high confidence level could easily be explained away by considering the fact that the joint influence of the positive variables such as F24, F92, F41, F67, and F56 far outshines the joint contribution of the negative variables such as F40, F60, F2, and F16. | [
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] | 54 | 2,550 | {'C2': '99.00%', 'C1': '1.00%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F21, F71 and F9?"
] | [
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] | {'F24': " Net Income to Stockholder's Equity", 'F41': ' Continuous interest rate (after tax)', 'F67': ' ROA(C) before interest and depreciation before interest', 'F40': ' Borrowing dependency', 'F92': ' Cash Flow Per Share', 'F56': ' Net worth\\/Assets', 'F78': ' Total income\\/Total expense', 'F21': ' Persistent EPS in the Last Four Seasons', 'F71': ' Retained Earnings to Total Assets', 'F9': ' Net Value Per Share (B)', 'F44': ' Cash Flow to Equity', 'F86': ' Net Value Per Share (A)', 'F6': ' Degree of Financial Leverage (DFL)', 'F60': ' Per Share Net profit before tax (Yuan ¥)', 'F2': ' Revenue Per Share (Yuan ¥)', 'F16': ' Inventory Turnover Rate (times)', 'F89': ' Net profit before tax\\/Paid-in capital', 'F73': ' Equity to Long-term Liability', 'F43': ' Operating profit\\/Paid-in capital', 'F61': ' Cash Turnover Rate', 'F59': ' Operating Funds to Liability', 'F90': ' Contingent liabilities\\/Net worth', 'F68': ' Working Capital to Total Assets', 'F76': ' Liability to Equity', 'F65': ' Current Liability to Liability', 'F75': ' Operating Gross Margin', 'F72': ' Operating Profit Per Share (Yuan ¥)', 'F49': ' Long-term Liability to Current Assets', 'F52': ' Current Asset Turnover Rate', 'F47': ' Interest Coverage Ratio (Interest expense to EBIT)', 'F45': ' Equity to Liability', 'F4': ' Operating Profit Rate', 'F81': ' Current Liability to Equity', 'F54': ' No-credit Interval', 'F7': ' Net Worth Turnover Rate (times)', 'F27': ' Working Capital\\/Equity', 'F87': ' Quick Assets\\/Current Liability', 'F48': ' Inventory and accounts receivable\\/Net value', 'F77': ' Current Liability to Current Assets', 'F22': ' Working capitcal Turnover Rate', 'F10': ' Fixed Assets to Assets', 'F50': ' Continuous Net Profit Growth Rate', 'F64': ' Cash Reinvestment %', 'F57': ' CFO to Assets', 'F36': ' Total Asset Turnover', 'F39': ' After-tax net Interest Rate', 'F33': ' After-tax Net Profit Growth Rate', 'F70': ' Tax rate (A)', 'F30': ' Current Ratio', 'F93': ' Realized Sales Gross Margin', 'F63': ' Net Value Per Share (C)', 'F79': ' Regular Net Profit Growth Rate', 'F5': ' Interest-bearing debt interest rate', 'F91': ' Debt ratio %', 'F38': ' Long-term fund suitability ratio (A)', 'F62': ' Net Value Growth Rate', 'F25': ' Total Asset Growth Rate', 'F20': ' Fixed Assets Turnover Frequency', 'F83': ' Inventory\\/Current Liability', 'F80': ' Allocation rate per person', 'F46': ' Operating Expense Rate', 'F17': ' Operating profit per person', 'F19': ' Net Income to Total Assets', 'F82': ' Interest Expense Ratio', 'F88': ' Cash\\/Total Assets', 'F29': ' ROA(B) before interest and depreciation after tax', 'F26': ' Inventory\\/Working Capital', 'F23': ' Total assets to GNP price', 'F51': ' Total debt\\/Total net worth', 'F3': ' Quick Ratio', 'F37': ' Revenue per person', 'F11': ' Non-industry income and expenditure\\/revenue', 'F58': ' Cash Flow to Sales', 'F66': ' ROA(A) before interest and % after tax', 'F53': ' Current Liabilities\\/Liability', 'F85': ' Operating Profit Growth Rate', 'F12': ' Cash Flow to Liability', 'F35': ' Cash Flow to Total Assets', 'F1': ' Pre-tax net Interest Rate', 'F28': ' Accounts Receivable Turnover', 'F34': ' Current Liability to Assets', 'F84': ' Quick Assets\\/Total Assets', 'F74': ' Total expense\\/Assets', 'F31': ' Average Collection Days', 'F15': ' Research and development expense rate', 'F14': ' Current Assets\\/Total Assets', 'F18': ' Current Liabilities\\/Equity', 'F69': ' Realized Sales Gross Profit Growth Rate', 'F55': ' Cash flow rate', 'F13': ' Total Asset Return Growth Rate Ratio', 'F42': ' Quick Asset Turnover Rate', 'F32': ' Cash\\/Current Liability', 'F8': ' Gross Profit to Sales'} | {'F59': 'F24', 'F12': 'F41', 'F29': 'F67', 'F3': 'F40', 'F65': 'F92', 'F84': 'F56', 'F57': 'F78', 'F8': 'F21', 'F10': 'F71', 'F27': 'F9', 'F53': 'F44', 'F42': 'F86', 'F35': 'F6', 'F78': 'F60', 'F31': 'F2', 'F18': 'F16', 'F72': 'F89', 'F23': 'F73', 'F89': 'F43', 'F34': 'F61', 'F87': 'F59', 'F64': 'F90', 'F67': 'F68', 'F66': 'F76', 'F90': 'F65', 'F62': 'F75', 'F63': 'F72', 'F69': 'F49', 'F61': 'F52', 'F60': 'F47', 'F91': 'F45', 'F58': 'F4', 'F92': 'F81', 'F56': 'F54', 'F55': 'F7', 'F68': 'F27', 'F71': 'F87', 'F70': 'F48', 'F86': 'F77', 'F73': 'F22', 'F74': 'F10', 'F54': 'F50', 'F75': 'F64', 'F76': 'F57', 'F77': 'F36', 'F79': 'F39', 'F80': 'F33', 'F81': 'F70', 'F82': 'F30', 'F83': 'F93', 'F88': 'F63', 'F85': 'F79', 'F1': 'F5', 'F47': 'F91', 'F52': 'F38', 'F15': 'F62', 'F24': 'F25', 'F22': 'F20', 'F21': 'F83', 'F20': 'F80', 'F19': 'F46', 'F17': 'F17', 'F16': 'F19', 'F14': 'F82', 'F26': 'F88', 'F13': 'F29', 'F11': 'F26', 'F9': 'F23', 'F7': 'F51', 'F6': 'F3', 'F5': 'F37', 'F4': 'F11', 'F25': 'F58', 'F28': 'F66', 'F51': 'F53', 'F43': 'F85', 'F50': 'F12', 'F49': 'F35', 'F48': 'F1', 'F2': 'F28', 'F46': 'F34', 'F45': 'F84', 'F44': 'F74', 'F41': 'F31', 'F30': 'F15', 'F40': 'F14', 'F39': 'F18', 'F38': 'F69', 'F37': 'F55', 'F36': 'F13', 'F33': 'F42', 'F32': 'F32', 'F93': 'F8'} | {'C1': 'C2', 'C2': 'C1'} | No | {'C2': 'No', 'C1': 'Yes'} |
RandomForestClassifier | C1 | House Price Classification | Between the two classes, the model labelled this case as C1 with a likelihood of about 97.0% since there is only a marginal chance that it belongs to label C2. The most relevant features influencing this decision are F7, F8, F6, and F3. In this case, F7, F8, and F3 have a considerable positive influence on the prediction of C1. In contrast, the values of F6 and F11 throw a bit of doubt on the C1 prediction. However, compared to F7, F8, and F3, this shift is very small. Finally, there are some attributes with limited impact on the prediction of C1 and these are F12, F9, F10, F1, F4, and F13 since their values are less important to the model in terms of determining the label for this case. | [
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] | 125 | 2,354 | {'C2': '3.00%', 'C1': '97.00%'} | [
"Summarize the prediction for the given test example?",
"For this test case, summarize the top features influencing the model's decision.",
"For these top features, what are the respective directions of influence on the prediction?",
"Provide a statement on the set of features has limited impact on the prediction of C1 by the model for the given test example?"
] | [
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"F3",
"F6",
"F5",
"F11",
"F2",
"F12",
"F9",
"F10",
"F1",
"F4",
"F13"
] | {'F7': 'LSTAT', 'F8': 'RM', 'F3': 'AGE', 'F6': 'TAX', 'F5': 'PTRATIO', 'F11': 'DIS', 'F2': 'CRIM', 'F12': 'RAD', 'F9': 'B', 'F10': 'NOX', 'F1': 'ZN', 'F4': 'INDUS', 'F13': 'CHAS'} | {'F13': 'F7', 'F6': 'F8', 'F7': 'F3', 'F10': 'F6', 'F11': 'F5', 'F8': 'F11', 'F1': 'F2', 'F9': 'F12', 'F12': 'F9', 'F5': 'F10', 'F2': 'F1', 'F3': 'F4', 'F4': 'F13'} | {'C2': 'C2', 'C1': 'C1'} | High | {'C2': 'Low', 'C1': 'High'} |
LogisticRegression | C1 | Concrete Strength Classification | Probably C1 is the right label for this case since the probability of the alternative label, C3 and C2, are only 1.03% and 0.0%. The order of importance of the features for the above classification verdict is F4, F5, F1, F7, F3, F6, F8, and F2. Analysis conducted shows that only the features F5, F3, and F6 have negative contributions, hence reducing the probability of assigning label C1 to the given case. Positive features that increase the likelihood that C1 is the valid label are F4, F1, F7, F8, and F2. The co-attribution of the positive variables is stronger than that of the negative ones, so it is not surprising that we see the level of confidence associated with the prediction of class C1. | [
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] | [
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] | 178 | 2,503 | {'C3': '1.03%', 'C1': '98.97%', 'C2': '0.0%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F4, F5 and F1.",
"Summarize the direction of influence of the features (F7, F3 and F6) with moderate impact on the prediction made for this test case.",
"Provide a statement on the features with the least impact on the prediction made for this test case."
] | [
"F4",
"F5",
"F1",
"F7",
"F3",
"F6",
"F8",
"F2"
] | {'F4': 'cement', 'F5': 'age_days', 'F1': 'water', 'F7': 'superplasticizer', 'F3': 'fineaggregate', 'F6': 'flyash', 'F8': 'slag', 'F2': 'coarseaggregate'} | {'F1': 'F4', 'F8': 'F5', 'F4': 'F1', 'F5': 'F7', 'F7': 'F3', 'F3': 'F6', 'F2': 'F8', 'F6': 'F2'} | {'C3': 'C3', 'C1': 'C1', 'C2': 'C2'} | Strong | {'C3': 'Weak', 'C1': 'Strong', 'C2': 'Other'} |
DNN | C1 | Credit Card Fraud Classification | The model labels the given data as C1 since it has a higher predicted probability equal to 51.42% compared to that of C2 which is equal to 48.58%. The input variables with higher contributions to the above classification decision are F26, F6, F18, F14, and F19, while those with little influence are F25, F30, F9, F3, and F5. Positively supporting the choice of the label, in this case, are mainly F26, F6, F19, and F14. However, the main negative variables are F18, F12, and F28. Judging based on the degree of influence as well as the direction of influence of the variables, it is not surprising that the model is only 51.42% confident in the assigned label which is marginally above average. | [
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] | 241 | 2,443 | {'C2': '48.58%', 'C1': '51.42%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F13, F10, F7 and F20?"
] | [
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"F25",
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] | {'F6': 'Z18', 'F26': 'Z14', 'F18': 'Time', 'F14': 'Z1', 'F19': 'Z19', 'F11': 'Z10', 'F13': 'Z4', 'F10': 'Z3', 'F7': 'Z12', 'F20': 'Z16', 'F12': 'Z7', 'F28': 'Z11', 'F4': 'Z9', 'F24': 'Z6', 'F22': 'Z23', 'F27': 'Z5', 'F16': 'Z17', 'F17': 'Z21', 'F23': 'Z24', 'F21': 'Z8', 'F2': 'Amount', 'F8': 'Z20', 'F29': 'Z27', 'F1': 'Z25', 'F15': 'Z13', 'F25': 'Z2', 'F30': 'Z22', 'F9': 'Z28', 'F3': 'Z26', 'F5': 'Z15'} | {'F19': 'F6', 'F15': 'F26', 'F1': 'F18', 'F2': 'F14', 'F20': 'F19', 'F11': 'F11', 'F5': 'F13', 'F4': 'F10', 'F13': 'F7', 'F17': 'F20', 'F8': 'F12', 'F12': 'F28', 'F10': 'F4', 'F7': 'F24', 'F24': 'F22', 'F6': 'F27', 'F18': 'F16', 'F22': 'F17', 'F25': 'F23', 'F9': 'F21', 'F30': 'F2', 'F21': 'F8', 'F28': 'F29', 'F26': 'F1', 'F14': 'F15', 'F3': 'F25', 'F23': 'F30', 'F29': 'F9', 'F27': 'F3', 'F16': 'F5'} | {'C1': 'C2', 'C2': 'C1'} | Fraud | {'C2': 'Not Fraud', 'C1': 'Fraud'} |
SGDClassifier | C1 | Company Bankruptcy Prediction | The following is the classification for the provided data: C1 is the most likely class label and C2 cannot possibly be the correct label given the likelihood is 0.0%. F48, F34, and F66 are the key variables that contributed to the classification choice. However, the classifier does not consider all features while making this conclusion, and these irrelevant features include F32, F39, F7, and F42. Revealed to have positive contributions to the prediction made here among the top features are F66, F2, F22, and F21, but all of the others, F48, F34, F92, F5, F1, F25, and F18, argue against labelling the present scenario as C2 and despite the fact that the bulk of relevant features are pointing in the opposite direction, the classifier is extremely certain that the proper label for the current scenario is C1, not C2. | [
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] | 257 | 2,632 | {'C2': '0.00%', 'C1': '100.00%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F48 and F34.",
"Summarize the direction of influence of the features (F66, F92, F18 and F21) with moderate impact on the prediction made for this test case.",
"Provide a statement on the features with the least impact on the prediction made for this test case."
] | [
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] | {'F48': ' Liability to Equity', 'F34': ' Net worth\\/Assets', 'F66': ' Debt ratio %', 'F92': " Net Income to Stockholder's Equity", 'F18': ' Equity to Liability', 'F21': ' Realized Sales Gross Margin', 'F5': ' Net Value Per Share (A)', 'F1': ' Current Liability to Assets', 'F2': ' Current Liability to Equity', 'F22': ' Net Income to Total Assets', 'F25': ' Operating Profit Per Share (Yuan ¥)', 'F90': ' ROA(B) before interest and depreciation after tax', 'F38': ' Working Capital to Total Assets', 'F54': ' Persistent EPS in the Last Four Seasons', 'F93': ' Current Liabilities\\/Equity', 'F72': ' Total expense\\/Assets', 'F43': ' Net Value Per Share (C)', 'F29': ' Gross Profit to Sales', 'F68': ' Pre-tax net Interest Rate', 'F30': ' Cash\\/Current Liability', 'F32': ' Total assets to GNP price', 'F39': ' Working capitcal Turnover Rate', 'F7': ' Net profit before tax\\/Paid-in capital', 'F42': ' Quick Assets\\/Current Liability', 'F87': ' Inventory and accounts receivable\\/Net value', 'F46': ' Long-term Liability to Current Assets', 'F80': ' Working Capital\\/Equity', 'F27': ' Operating Expense Rate', 'F60': ' Cash Reinvestment %', 'F74': ' Retained Earnings to Total Assets', 'F56': ' Cash Flow Per Share', 'F14': ' Contingent liabilities\\/Net worth', 'F67': ' Inventory\\/Working Capital', 'F15': ' Operating Gross Margin', 'F86': ' Current Asset Turnover Rate', 'F49': ' Interest Coverage Ratio (Interest expense to EBIT)', 'F83': ' Fixed Assets to Assets', 'F52': ' CFO to Assets', 'F85': ' Operating Profit Rate', 'F10': ' Total Asset Turnover', 'F17': ' Borrowing dependency', 'F82': ' Non-industry income and expenditure\\/revenue', 'F36': ' Current Liability to Liability', 'F37': ' Operating profit\\/Paid-in capital', 'F35': ' Revenue per person', 'F89': ' Operating Funds to Liability', 'F50': ' Current Liability to Current Assets', 'F59': ' Regular Net Profit Growth Rate', 'F3': ' Quick Ratio', 'F12': ' Total debt\\/Total net worth', 'F33': ' Current Ratio', 'F62': ' Tax rate (A)', 'F65': ' After-tax Net Profit Growth Rate', 'F40': ' After-tax net Interest Rate', 'F57': ' Per Share Net profit before tax (Yuan ¥)', 'F73': ' Continuous interest rate (after tax)', 'F9': ' No-credit Interval', 'F8': ' Total income\\/Total expense', 'F69': ' Allocation rate per person', 'F81': ' Total Asset Return Growth Rate Ratio', 'F24': ' Degree of Financial Leverage (DFL)', 'F76': ' Cash Turnover Rate', 'F51': ' Quick Asset Turnover Rate', 'F77': ' Revenue Per Share (Yuan ¥)', 'F6': ' Research and development expense rate', 'F53': ' ROA(C) before interest and depreciation before interest', 'F64': ' ROA(A) before interest and % after tax', 'F4': ' Net Value Per Share (B)', 'F75': ' Cash\\/Total Assets', 'F13': ' Cash Flow to Sales', 'F16': ' Total Asset Growth Rate', 'F55': ' Equity to Long-term Liability', 'F11': ' Fixed Assets Turnover Frequency', 'F63': ' Inventory\\/Current Liability', 'F91': ' Cash flow rate', 'F28': ' Realized Sales Gross Profit Growth Rate', 'F84': ' Inventory Turnover Rate (times)', 'F20': ' Cash Flow to Total Assets', 'F31': ' Net Worth Turnover Rate (times)', 'F19': ' Continuous Net Profit Growth Rate', 'F70': ' Cash Flow to Equity', 'F41': ' Long-term fund suitability ratio (A)', 'F88': ' Current Liabilities\\/Liability', 'F79': ' Cash Flow to Liability', 'F78': ' Accounts Receivable Turnover', 'F44': ' Current Assets\\/Total Assets', 'F26': ' Interest Expense Ratio', 'F45': ' Quick Assets\\/Total Assets', 'F58': ' Net Value Growth Rate', 'F61': ' Operating Profit Growth Rate', 'F23': ' Operating profit per person', 'F71': ' Average Collection Days', 'F47': ' Interest-bearing debt interest rate'} | {'F66': 'F48', 'F84': 'F34', 'F47': 'F66', 'F59': 'F92', 'F91': 'F18', 'F83': 'F21', 'F42': 'F5', 'F46': 'F1', 'F92': 'F2', 'F16': 'F22', 'F63': 'F25', 'F13': 'F90', 'F67': 'F38', 'F8': 'F54', 'F39': 'F93', 'F44': 'F72', 'F88': 'F43', 'F93': 'F29', 'F48': 'F68', 'F32': 'F30', 'F9': 'F32', 'F73': 'F39', 'F72': 'F7', 'F71': 'F42', 'F70': 'F87', 'F69': 'F46', 'F68': 'F80', 'F19': 'F27', 'F75': 'F60', 'F10': 'F74', 'F65': 'F56', 'F64': 'F14', 'F11': 'F67', 'F62': 'F15', 'F61': 'F86', 'F60': 'F49', 'F74': 'F83', 'F76': 'F52', 'F58': 'F85', 'F77': 'F10', 'F3': 'F17', 'F4': 'F82', 'F90': 'F36', 'F89': 'F37', 'F5': 'F35', 'F87': 'F89', 'F86': 'F50', 'F85': 'F59', 'F6': 'F3', 'F7': 'F12', 'F82': 'F33', 'F81': 'F62', 'F80': 'F65', 'F79': 'F40', 'F78': 'F57', 'F12': 'F73', 'F56': 'F9', 'F57': 'F8', 'F20': 'F69', 'F36': 'F81', 'F35': 'F24', 'F34': 'F76', 'F33': 'F51', 'F31': 'F77', 'F30': 'F6', 'F29': 'F53', 'F28': 'F64', 'F27': 'F4', 'F26': 'F75', 'F25': 'F13', 'F24': 'F16', 'F23': 'F55', 'F22': 'F11', 'F21': 'F63', 'F37': 'F91', 'F38': 'F28', 'F18': 'F84', 'F49': 'F20', 'F55': 'F31', 'F54': 'F19', 'F53': 'F70', 'F52': 'F41', 'F51': 'F88', 'F50': 'F79', 'F2': 'F78', 'F40': 'F44', 'F14': 'F26', 'F45': 'F45', 'F15': 'F58', 'F43': 'F61', 'F17': 'F23', 'F41': 'F71', 'F1': 'F47'} | {'C2': 'C2', 'C1': 'C1'} | Yes | {'C2': 'No', 'C1': 'Yes'} |
GradientBoostingClassifier | C1 | Food Ordering Customer Churn Prediction | The prediction probability of C2 is 17.93% and that of C1 is 82.07%. Therefore, the most probable class for the given case is C1. The above classification assertion statements are based on the information supplied to the classifier about the case given. The top features with significant attributions leading to the decision made above are F38, F6, F18, F33, F23, and F10. Conversely, F25, F17, F42, F12, and F11 are among the features deemed irrelevant to the classification decision here since their contributions are almost negligible and much closer to zero. The attribution analysis suggests that not all the relevant features positively contribute to the classifier's arriving at the verdict here. Those with positive attributions that push the classifier towards generating C1 as the label are F38, F6, F18, F33, F41, F14, F28, and F31. Decreasing the likelihood of the correctness of C1 are the negative features such as F23, F21, F10, F2, F40, F4, F29, and F43, which could be blamed for the little uncertainty in the classification output, as indicated by the prediction probability of C2. | [
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] | 7 | 2,658 | {'C2': '17.93%', 'C1': '82.07%'} | [
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Summarize the direction of influence of the features (F38 (when it is equal to V1), F6 (value equal to V1), F18 (equal to V0), F33 (when it is equal to V1) and F10 (when it is equal to V3)) on the prediction made for this test case.",
"Compare the direction of impact of the features: F23 (with a value equal to V1), F21 (with a value equal to V3) and F14 (equal to V2).",
"Describe the degree of impact of the following features: F40 (equal to V2), F4 (when it is equal to V0) and F2 (when it is equal to V3)?"
] | [
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] | {'F38': 'More restaurant choices', 'F6': 'Ease and convenient', 'F18': 'Bad past experience', 'F33': 'Time saving', 'F10': 'Unaffordable', 'F23': 'Educational Qualifications', 'F21': 'Late Delivery', 'F14': 'Occupation', 'F40': 'Influence of rating', 'F4': 'Less Delivery time', 'F2': 'Order placed by mistake', 'F29': 'Delivery person ability', 'F43': 'Order Time', 'F41': 'Unavailability', 'F30': 'More Offers and Discount', 'F35': 'Delay of delivery person picking up food', 'F28': 'Good Taste ', 'F31': 'Wrong order delivered', 'F7': 'Freshness ', 'F20': 'Missing item', 'F25': 'Residence in busy location', 'F42': 'Google Maps Accuracy', 'F17': 'Age', 'F11': 'Good Road Condition', 'F12': 'Low quantity low time', 'F45': 'High Quality of package', 'F1': 'Number of calls', 'F22': 'Politeness', 'F15': 'Temperature', 'F27': 'Maximum wait time', 'F36': 'Long delivery time', 'F5': 'Influence of time', 'F37': 'Delay of delivery person getting assigned', 'F26': 'Family size', 'F32': 'Poor Hygiene', 'F44': 'Health Concern', 'F3': 'Self Cooking', 'F19': 'Good Tracking system', 'F8': 'Good Food quality', 'F39': 'Easy Payment option', 'F46': 'Perference(P2)', 'F16': 'Perference(P1)', 'F34': 'Monthly Income', 'F9': 'Marital Status', 'F13': 'Gender', 'F24': 'Good Quantity'} | {'F12': 'F38', 'F10': 'F6', 'F21': 'F18', 'F11': 'F33', 'F23': 'F10', 'F6': 'F23', 'F19': 'F21', 'F4': 'F14', 'F38': 'F40', 'F39': 'F4', 'F29': 'F2', 'F37': 'F29', 'F31': 'F43', 'F22': 'F41', 'F14': 'F30', 'F26': 'F35', 'F45': 'F28', 'F27': 'F31', 'F43': 'F7', 'F28': 'F20', 'F33': 'F25', 'F34': 'F42', 'F1': 'F17', 'F35': 'F11', 'F36': 'F12', 'F40': 'F45', 'F41': 'F1', 'F42': 'F22', 'F44': 'F15', 'F32': 'F27', 'F24': 'F36', 'F30': 'F5', 'F25': 'F37', 'F7': 'F26', 'F20': 'F32', 'F18': 'F44', 'F17': 'F3', 'F16': 'F19', 'F15': 'F8', 'F13': 'F39', 'F9': 'F46', 'F8': 'F16', 'F5': 'F34', 'F3': 'F9', 'F2': 'F13', 'F46': 'F24'} | {'C1': 'C2', 'C2': 'C1'} | Go Away | {'C2': 'Return', 'C1': 'Go Away'} |
DNN | C2 | Credit Card Fraud Classification | The classification algorithm classifies the given case as C2 with a confidence level equal to 99.99%, suggesting that there is little chance that the C1 label could be the true label. The classification confidence level can be attributed to the influence and contributions of the features F30, F17, F26, F16, and F9. Positively supporting the model's decision are values of F30, F17, F26, and F16. On the contrary, the values of F9, F1, F8, and F7 are shifting the model towards producing the C1 label, which results in a marginal decrease in the certainty associated with the C2 label. The other positively supported features further improving the odds in favour of C2 include F14, F19, F15, and F24. Overall, it is not farfetched to accept that C2 is the correct label for the case under consideration since the strong positive influences of F30, F17, and F26 far outweigh the influence of any of the other input features. In other words, as mentioned above, there is only a small chance that the true label is not C2 considering the attributions of the top influential input features. | [
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"Provide a statement summarizing the prediction made for the test case.",
"For the current test instance, describe the direction of influence of the following features: F30 and F17.",
"Compare and contrast the impact of the following features (F26, F9, F16 and F1) on the model’s prediction of C2.",
"Describe the degree of impact of the following features: F7, F14 and F8?"
] | [
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"F24",
"F3",
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"F20",
"F25",
"F18",
"F13",
"F5",
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] | {'F30': 'Z3', 'F17': 'Z6', 'F26': 'Time', 'F9': 'Z13', 'F16': 'Z12', 'F1': 'Z4', 'F7': 'Z10', 'F14': 'Z5', 'F8': 'Z9', 'F21': 'Z14', 'F29': 'Z16', 'F27': 'Z11', 'F19': 'Z17', 'F6': 'Z19', 'F15': 'Z8', 'F11': 'Z28', 'F22': 'Z21', 'F28': 'Z20', 'F4': 'Z1', 'F12': 'Z24', 'F10': 'Z18', 'F24': 'Z2', 'F3': 'Z25', 'F23': 'Amount', 'F20': 'Z26', 'F25': 'Z27', 'F18': 'Z22', 'F13': 'Z15', 'F5': 'Z7', 'F2': 'Z23'} | {'F4': 'F30', 'F7': 'F17', 'F1': 'F26', 'F14': 'F9', 'F13': 'F16', 'F5': 'F1', 'F11': 'F7', 'F6': 'F14', 'F10': 'F8', 'F15': 'F21', 'F17': 'F29', 'F12': 'F27', 'F18': 'F19', 'F20': 'F6', 'F9': 'F15', 'F29': 'F11', 'F22': 'F22', 'F21': 'F28', 'F2': 'F4', 'F25': 'F12', 'F19': 'F10', 'F3': 'F24', 'F26': 'F3', 'F30': 'F23', 'F27': 'F20', 'F28': 'F25', 'F23': 'F18', 'F16': 'F13', 'F8': 'F5', 'F24': 'F2'} | {'C1': 'C2', 'C2': 'C1'} | Not Fraud | {'C2': 'Not Fraud', 'C1': 'Fraud'} |
RandomForestClassifier | C1 | Employee Attrition | The data is marked as C1 by the classifier based on the input features, with a moderate degree of confidence since the prediction probability of the other label, C2, is only 44.0%. The most influential features driving the classification above are F6, F20, F27, F21, F19, F9, F29, F7, F23, F8, F22, F10, F3, F11, F12, F4, F1, F30, and F28. Strongly reducing the chance of C1 being the true label for the given case are the negative features F20 and F6. Actually, these negative features, along with other features such as F19, F9, and F23, are responsible for the uncertainty in the classification decision here. On the contrary, the input features F27, F21, F29, F7, F8, and F22 positively contribute to the classifier's decision to choose C1 as the label here. Finally, it is important to note that not all the features are shown to be relevant when making the labelling decision regarding the case under consideration, and these irrelevant features include F24, F18, F15, and F14. | [
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] | 27 | 2,678 | {'C2': '44.00%', 'C1': '56.00%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F27 (value equal to V2), F21 (value equal to V1), F19 (with a value equal to V2) and F9 (when it is equal to V2)) on the model’s prediction of C1.",
"Summarize the set of features has little to no impact on the prediction?"
] | [
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"F14",
"F16",
"F15",
"F13",
"F25",
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] | {'F6': 'OverTime', 'F20': 'BusinessTravel', 'F27': 'MaritalStatus', 'F21': 'JobInvolvement', 'F19': 'WorkLifeBalance', 'F9': 'Education', 'F29': 'EnvironmentSatisfaction', 'F7': 'Gender', 'F23': 'JobRole', 'F8': 'NumCompaniesWorked', 'F22': 'YearsInCurrentRole', 'F10': 'HourlyRate', 'F3': 'Department', 'F11': 'RelationshipSatisfaction', 'F12': 'PerformanceRating', 'F17': 'YearsWithCurrManager', 'F4': 'Age', 'F1': 'MonthlyRate', 'F30': 'StockOptionLevel', 'F28': 'JobSatisfaction', 'F24': 'DailyRate', 'F5': 'YearsSinceLastPromotion', 'F2': 'YearsAtCompany', 'F14': 'TrainingTimesLastYear', 'F16': 'EducationField', 'F15': 'TotalWorkingYears', 'F13': 'PercentSalaryHike', 'F25': 'MonthlyIncome', 'F26': 'JobLevel', 'F18': 'DistanceFromHome'} | {'F26': 'F6', 'F17': 'F20', 'F25': 'F27', 'F29': 'F21', 'F20': 'F19', 'F27': 'F9', 'F28': 'F29', 'F23': 'F7', 'F24': 'F23', 'F8': 'F8', 'F14': 'F22', 'F4': 'F10', 'F21': 'F3', 'F18': 'F11', 'F19': 'F12', 'F16': 'F17', 'F1': 'F4', 'F7': 'F1', 'F10': 'F30', 'F30': 'F28', 'F2': 'F24', 'F15': 'F5', 'F13': 'F2', 'F12': 'F14', 'F22': 'F16', 'F11': 'F15', 'F9': 'F13', 'F6': 'F25', 'F5': 'F26', 'F3': 'F18'} | {'C1': 'C2', 'C2': 'C1'} | Leave | {'C2': 'Leave', 'C1': 'Leave'} |
RandomForestClassifier | C3 | Cab Surge Pricing System | The model determined that this case belongs to C3 of the three possible labels, with an 83.0% likelihood. It is important to note, however, that there is about a 14.0% chance that it could be C2 and a 3.0% chance that it is rather C1. The most relevant feature driving this prediction is F1, with a very strong positive attribution, increasing the odds of the label C3. The following attributes have values pushing for a different prediction: F4, F12, F6, and F5, however, their attributions are very low when compared to that from F1. Other features positively contributing to the model's decision for this test case are F9, F10, F7, F8, F2, F3, and F11, with F2, F3, and F11 being the least relevant features considered by the model for the given case. | [
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] | 124 | 2,353 | {'C1': '3.00%', 'C3': '83.00%', 'C2': '14.00%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F12, F6, F9 (when it is equal to V2) and F10) on the model’s prediction of C3.",
"Summarize the set of features has little to no impact on the prediction?"
] | [
"F1",
"F4",
"F12",
"F6",
"F9",
"F10",
"F5",
"F7",
"F8",
"F2",
"F3",
"F11"
] | {'F1': 'Type_of_Cab', 'F4': 'Destination_Type', 'F12': 'Trip_Distance', 'F6': 'Cancellation_Last_1Month', 'F9': 'Confidence_Life_Style_Index', 'F10': 'Var3', 'F5': 'Customer_Since_Months', 'F7': 'Life_Style_Index', 'F8': 'Var2', 'F2': 'Gender', 'F3': 'Var1', 'F11': 'Customer_Rating'} | {'F2': 'F1', 'F6': 'F4', 'F1': 'F12', 'F8': 'F6', 'F5': 'F9', 'F11': 'F10', 'F3': 'F5', 'F4': 'F7', 'F10': 'F8', 'F12': 'F2', 'F9': 'F3', 'F7': 'F11'} | {'C3': 'C1', 'C1': 'C3', 'C2': 'C2'} | C2 | {'C1': 'Low', 'C3': 'Medium', 'C2': 'High'} |
Subsets and Splits