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james-burton

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aug-text-exps-v3

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SGDClassifier

C2

House Price Classification

The classifier's anticipated label for this case is C2 which is a decision that it is highly confident about since the predicted likelihood is 100.0%. The most important variables are F2, F4, F6, and F3, whose values lead to the aforesaid classification conclusion. Under this classification instance, examination of the attributions of the features showed that F12, F11, and F13 are the least essential features. Because majority of the case's attributes positively validate the assigned label, it's not unexpected that the classifier picked the C2. F2, F4, F3, F5, F10, and F9 are all positive variables, while F6, F8, and F7 are three contradicting variables that moderately drive the labelling judgment towards C1.

{'C2': '100.00%', 'C1': '0.00%'}

{'F2': 'CRIM', 'F4': 'LSTAT', 'F6': 'RAD', 'F3': 'AGE', 'F9': 'CHAS', 'F8': 'DIS', 'F5': 'ZN', 'F10': 'TAX', 'F1': 'PTRATIO', 'F7': 'B', 'F12': 'RM', 'F11': 'NOX', 'F13': 'INDUS'}

{'F1': 'F2', 'F13': 'F4', 'F9': 'F6', 'F7': 'F3', 'F4': 'F9', 'F8': 'F8', 'F2': 'F5', 'F10': 'F10', 'F11': 'F1', 'F12': 'F7', 'F6': 'F12', 'F5': 'F11', 'F3': 'F13'}

{'C2': 'C2', 'C1': 'C1'}

Low

{'C2': 'Low', 'C1': 'High'}

GaussianNB

C2

Tic-Tac-Toe Strategy

The model selects C2 as the correct label with a probability of 57.58%, while the other class, C1, has a slightly lower probability of 42.42%. The most relevant attribute is F2, followed by F7, F6, F9, F4, F3, F8, F1 and finally F5, which is the least relevant. The features F4, F8, and F2 have a positive influence, increasing the probability of the classification output, while F6 has a negative attribution, swinging the model to assign C1 instead. F9, F1, F7, and F3 are some of the other negative attributes. Finally, F5 has a very small positive control over the prediction in this test case but it further increases the confidence in the label chosen for the given case.

{'C2': '57.58%', 'C1': '42.42%'}

{'F2': 'middle-middle-square', 'F6': 'top-left-square', 'F9': 'bottom-right-square', 'F1': ' top-right-square', 'F7': 'middle-left-square', 'F3': 'bottom-middle-square', 'F8': 'bottom-left-square', 'F4': 'middle-right-square', 'F5': 'top-middle-square'}

{'F5': 'F2', 'F1': 'F6', 'F9': 'F9', 'F3': 'F1', 'F4': 'F7', 'F8': 'F3', 'F7': 'F8', 'F6': 'F4', 'F2': 'F5'}

{'C2': 'C2', 'C1': 'C1'}

player B lose

{'C2': 'player B lose', 'C1': 'player B win'}

SGDClassifier

C2

House Price Classification

The prediction verdict here is that the most probable class label is C2. Actually, the classification algorithm indicates that there is no possibility that the correct label is C1. Majorly contributing to the above classification are F9, F4, F10, and F3, all with positive influence. It is therefore not surprising that the algorithm is confident that C2 is the right label. The other positive features considered to arrive at the decision here are F11, F12, F7, F13, and F6. According to the attribution analysis, only F1, F5, and F2 have negative contributions, which tend to attempt to swing the final verdict in favour of C1. To sum up, the joint negative influence is not enough to outweigh the positive features, hence the C2 is assigned for the given case.

{'C1': '0.00%', 'C2': '100.00%'}

{'F9': 'AGE', 'F4': 'RAD', 'F10': 'LSTAT', 'F3': 'RM', 'F1': 'DIS', 'F5': 'CHAS', 'F12': 'ZN', 'F11': 'CRIM', 'F8': 'TAX', 'F7': 'B', 'F13': 'PTRATIO', 'F6': 'INDUS', 'F2': 'NOX'}

{'F7': 'F9', 'F9': 'F4', 'F13': 'F10', 'F6': 'F3', 'F8': 'F1', 'F4': 'F5', 'F2': 'F12', 'F1': 'F11', 'F10': 'F8', 'F12': 'F7', 'F11': 'F13', 'F3': 'F6', 'F5': 'F2'}

{'C2': 'C1', 'C1': 'C2'}

High

{'C1': 'Low', 'C2': 'High'}

DecisionTreeClassifier

C1

Hotel Satisfaction

Due to the prediction probability distribution across the class labels, the labels assigned to this example is C1 with a high degree of confidence, close to 100 percent. The most significant features driving the classification above, according to the attributions of the input features, are F14, F15, F3, and F12. F10 and F4, on the other hand, are the least essential features to this prediction here. In addition, just four of the input features have a negative impact, skewing the classifier's judgement in favour of the C2 label. F10, F3, F6, and F4 are the opposing features. The contribution of the negative features, with the exception of F3, is quite modest when compared to the top positive features such as F15, F12, and F11.

{'C2': '0.00%', 'C1': '100.00%'}

{'F14': 'Type of Travel', 'F3': 'Hotel wifi service', 'F12': 'Other service', 'F15': 'Type Of Booking', 'F11': 'Checkin\\/Checkout service', 'F7': 'Age', 'F2': 'purpose_of_travel', 'F9': 'Common Room entertainment', 'F8': 'Food and drink', 'F6': 'Stay comfort', 'F13': 'Hotel location', 'F1': 'Departure\\/Arrival convenience', 'F5': 'Gender', 'F10': 'Ease of Online booking', 'F4': 'Cleanliness'}

{'F3': 'F14', 'F6': 'F3', 'F14': 'F12', 'F4': 'F15', 'F13': 'F11', 'F5': 'F7', 'F2': 'F2', 'F12': 'F9', 'F10': 'F8', 'F11': 'F6', 'F9': 'F13', 'F7': 'F1', 'F1': 'F5', 'F8': 'F10', 'F15': 'F4'}

{'C2': 'C2', 'C1': 'C1'}

satisfied

{'C2': 'dissatisfied', 'C1': 'satisfied'}

RandomForestClassifier

C2

Student Job Placement

The classification algorithm predicts that the data sample given should be classified as C2 with a probability of 76.06%, but it also finds that there is a 23.94% probability that the correct label will be C1. The positive influence of the F12, F6, F11, and F4 features on the algorithm supports the C2 class tasks. F2 and F7 are features with little positive influence on the classification decision for a particular case. F10 and F8, in contrast, has a small negative impact on the output decision that result in the reduction in the likelihood of C2 hence can be said to favour labelling the case as C1. F1 and F5 had only a minor positive impact on the final labelling decision and finally F9 was shown to have zero effect on the algorithm in this case.

{'C2': '76.06%', 'C1': '23.94%'}

{'F12': 'ssc_p', 'F6': 'hsc_p', 'F11': 'workex', 'F4': 'specialisation', 'F2': 'gender', 'F10': 'hsc_s', 'F7': 'degree_p', 'F5': 'etest_p', 'F8': 'degree_t', 'F1': 'ssc_b', 'F3': 'hsc_b', 'F9': 'mba_p'}

{'F1': 'F12', 'F2': 'F6', 'F11': 'F11', 'F12': 'F4', 'F6': 'F2', 'F9': 'F10', 'F3': 'F7', 'F4': 'F5', 'F10': 'F8', 'F7': 'F1', 'F8': 'F3', 'F5': 'F9'}

{'C1': 'C2', 'C2': 'C1'}

Not Placed

{'C2': 'Not Placed', 'C1': 'Placed'}

KNeighborsClassifier

C2

Credit Risk Classification

According to the model, there is a higher chance that the case's label is C2. This prediction decision is based primarily on the attribution of the following features: F10, F8, F4, and F7. Aside from F7, all the other features listed above have a strong positive influence, increasing the probability of the predicted class C2. Similar to F7, the values of features F2, F3, and F5 suggest the other label, C1, could be the correct label. However, unlike F10, F8, and F4, each of the negative features has a moderate contribution to the final decision. The remaining features F11, F9, and F6 are shown to have marginal contributions to the model's decision for this case, and F1 was ranked as the least important feature. In summary, with strong positive attributions from F10, F8, F4, and F11, the model is very certain about the classification verdict, with a certainty of 100.0%.

{'C2': '100.00%', 'C1': '0.00%'}

{'F10': 'fea_4', 'F8': 'fea_8', 'F4': 'fea_2', 'F7': 'fea_9', 'F2': 'fea_6', 'F3': 'fea_10', 'F5': 'fea_1', 'F11': 'fea_7', 'F9': 'fea_11', 'F6': 'fea_3', 'F1': 'fea_5'}

{'F4': 'F10', 'F8': 'F8', 'F2': 'F4', 'F9': 'F7', 'F6': 'F2', 'F10': 'F3', 'F1': 'F5', 'F7': 'F11', 'F11': 'F9', 'F3': 'F6', 'F5': 'F1'}

{'C1': 'C2', 'C2': 'C1'}

Low

{'C2': 'Low', 'C1': 'High'}

KNeighborsClassifier

C1

Company Bankruptcy Prediction

For the case under consideration, the model's output labelling decision is as follows: there is no possibility that C2 is the label for the given case, C1 is the most likely class label, with a confidence level close of 100.0%. The values of the input features, F14, F15, F64, F61, F35, F60, and F27, are the main driving forces resulting in the above classification. The features with moderate influence on the decision here are F82, F19, F54, F55, F32, F2, F69, F70, F88, F59, F77, F40, and F46. Apart from all the abovementioned input features, all the remaining ones, such as F50, F4, F17, and F16, are shown to be irrelevant to the decision made here. Also per the attribution analysis, not all the influential features support labelling the given case as C1, and these are referred to as negative features since they reduce the probability that C1 is the right label here and these are F27, F19, F77, F40, and F46. The notable positive features increasing the probability that C1 is the right label are F14, F15, F64, and F61.

{'C1': '100.00%', 'C2': '0.00%'}

{'F14': ' Interest Coverage Ratio (Interest expense to EBIT)', 'F15': ' Net Income to Total Assets', 'F64': ' Realized Sales Gross Profit Growth Rate', 'F61': ' Accounts Receivable Turnover', 'F35': ' Operating Expense Rate', 'F27': ' Contingent liabilities\\/Net worth', 'F60': ' Non-industry income and expenditure\\/revenue', 'F82': ' Current Ratio', 'F19': ' Cash Flow to Liability', 'F54': ' Fixed Assets Turnover Frequency', 'F32': ' Regular Net Profit Growth Rate', 'F55': ' Quick Asset Turnover Rate', 'F2': ' Net Value Per Share (C)', 'F69': ' Operating Profit Growth Rate', 'F70': ' After-tax Net Profit Growth Rate', 'F88': ' Continuous Net Profit Growth Rate', 'F59': ' Net Value Per Share (B)', 'F77': ' Equity to Long-term Liability', 'F40': ' CFO to Assets', 'F46': ' Total debt\\/Total net worth', 'F50': ' Current Asset Turnover Rate', 'F4': " Net Income to Stockholder's Equity", 'F17': ' Operating Gross Margin', 'F16': ' Operating Profit Per Share (Yuan ¥)', 'F73': ' Operating Profit Rate', 'F24': ' Cash Flow Per Share', 'F30': ' Total income\\/Total expense', 'F5': ' No-credit Interval', 'F11': ' Liability to Equity', 'F33': ' Working Capital to Total Assets', 'F91': ' Working Capital\\/Equity', 'F23': ' Long-term Liability to Current Assets', 'F8': ' Interest-bearing debt interest rate', 'F79': ' Inventory and accounts receivable\\/Net value', 'F26': ' Realized Sales Gross Margin', 'F7': ' Current Liability to Equity', 'F44': ' Equity to Liability', 'F22': ' Current Liability to Liability', 'F38': ' Operating profit\\/Paid-in capital', 'F25': ' Operating Funds to Liability', 'F13': ' Current Liability to Current Assets', 'F83': ' Net worth\\/Assets', 'F63': ' Tax rate (A)', 'F92': ' Quick Assets\\/Current Liability', 'F87': ' After-tax net Interest Rate', 'F43': ' Per Share Net profit before tax (Yuan ¥)', 'F21': ' Total Asset Turnover', 'F52': ' Cash Reinvestment %', 'F72': ' Fixed Assets to Assets', 'F45': ' Working capitcal Turnover Rate', 'F66': ' Net profit before tax\\/Paid-in capital', 'F48': ' Net Worth Turnover Rate (times)', 'F93': ' Debt ratio %', 'F18': ' Cash Flow to Equity', 'F39': ' Long-term fund suitability ratio (A)', 'F20': ' Cash Flow to Sales', 'F62': ' Total Asset Growth Rate', 'F84': ' Inventory\\/Current Liability', 'F56': ' Allocation rate per person', 'F86': ' Inventory Turnover Rate (times)', 'F47': ' Operating profit per person', 'F90': ' Net Value Growth Rate', 'F76': ' Interest Expense Ratio', 'F68': ' ROA(B) before interest and depreciation after tax', 'F29': ' Continuous interest rate (after tax)', 'F31': ' Inventory\\/Working Capital', 'F9': ' Retained Earnings to Total Assets', 'F42': ' Total assets to GNP price', 'F67': ' Persistent EPS in the Last Four Seasons', 'F74': ' Quick Ratio', 'F80': ' Revenue per person', 'F53': ' Borrowing dependency', 'F89': ' Cash\\/Total Assets', 'F41': ' ROA(A) before interest and % after tax', 'F28': ' ROA(C) before interest and depreciation before interest', 'F49': ' Average Collection Days', 'F81': ' Current Liabilities\\/Liability', 'F51': ' Cash Flow to Total Assets', 'F3': ' Pre-tax net Interest Rate', 'F37': ' Current Liability to Assets', 'F71': ' Quick Assets\\/Total Assets', 'F10': ' Total expense\\/Assets', 'F12': ' Net Value Per Share (A)', 'F1': ' Current Assets\\/Total Assets', 'F85': ' Research and development expense rate', 'F65': ' Current Liabilities\\/Equity', 'F78': ' Cash flow rate', 'F75': ' Total Asset Return Growth Rate Ratio', 'F34': ' Degree of Financial Leverage (DFL)', 'F57': ' Cash Turnover Rate', 'F36': ' Cash\\/Current Liability', 'F6': ' Revenue Per Share (Yuan ¥)', 'F58': ' Gross Profit to Sales'}

{'F60': 'F14', 'F16': 'F15', 'F38': 'F64', 'F2': 'F61', 'F19': 'F35', 'F64': 'F27', 'F4': 'F60', 'F82': 'F82', 'F50': 'F19', 'F22': 'F54', 'F85': 'F32', 'F33': 'F55', 'F88': 'F2', 'F43': 'F69', 'F80': 'F70', 'F54': 'F88', 'F27': 'F59', 'F23': 'F77', 'F76': 'F40', 'F7': 'F46', 'F61': 'F50', 'F59': 'F4', 'F62': 'F17', 'F63': 'F16', 'F58': 'F73', 'F65': 'F24', 'F57': 'F30', 'F56': 'F5', 'F66': 'F11', 'F67': 'F33', 'F68': 'F91', 'F69': 'F23', 'F1': 'F8', 'F70': 'F79', 'F83': 'F26', 'F92': 'F7', 'F91': 'F44', 'F90': 'F22', 'F89': 'F38', 'F87': 'F25', 'F86': 'F13', 'F84': 'F83', 'F81': 'F63', 'F71': 'F92', 'F79': 'F87', 'F78': 'F43', 'F77': 'F21', 'F75': 'F52', 'F74': 'F72', 'F73': 'F45', 'F72': 'F66', 'F55': 'F48', 'F47': 'F93', 'F53': 'F18', 'F52': 'F39', 'F25': 'F20', 'F24': 'F62', 'F21': 'F84', 'F20': 'F56', 'F18': 'F86', 'F17': 'F47', 'F15': 'F90', 'F14': 'F76', 'F13': 'F68', 'F12': 'F29', 'F11': 'F31', 'F10': 'F9', 'F9': 'F42', 'F8': 'F67', 'F6': 'F74', 'F5': 'F80', 'F3': 'F53', 'F26': 'F89', 'F28': 'F41', 'F29': 'F28', 'F41': 'F49', 'F51': 'F81', 'F49': 'F51', 'F48': 'F3', 'F46': 'F37', 'F45': 'F71', 'F44': 'F10', 'F42': 'F12', 'F40': 'F1', 'F30': 'F85', 'F39': 'F65', 'F37': 'F78', 'F36': 'F75', 'F35': 'F34', 'F34': 'F57', 'F32': 'F36', 'F31': 'F6', 'F93': 'F58'}

{'C2': 'C1', 'C1': 'C2'}

No

{'C1': 'No', 'C2': 'Yes'}

KNeighborsClassifier

C2

Wine Quality Prediction

Based on the influence of features such as F8, F2, F11, and F9, the classifier is pretty confident that the correct label for the given data is C2, whilst, there is a 10.0% probability that the proper label could be C1. The majority of the features have positive contributions, while only F9, F10, and F5 are the negative features, decreasing the classifier's response towards choosing C2. The notal positive features that increase the classifier's response higher towards label C2 instead of C1 include F8, F2, F6, F3, F1, and F11. Taking into consideration the attributions of the input features, we can attribute the classifier's confidence associated with this prediction to the fact that the negative features only have a moderate impact on the classifier's decision for the given data.

{'C1': '10.00%', 'C2': '90.00%'}

{'F8': 'sulphates', 'F2': 'total sulfur dioxide', 'F11': 'volatile acidity', 'F9': 'residual sugar', 'F6': 'citric acid', 'F3': 'chlorides', 'F1': 'alcohol', 'F4': 'fixed acidity', 'F10': 'density', 'F7': 'pH', 'F5': 'free sulfur dioxide'}

{'F10': 'F8', 'F7': 'F2', 'F2': 'F11', 'F4': 'F9', 'F3': 'F6', 'F5': 'F3', 'F11': 'F1', 'F1': 'F4', 'F8': 'F10', 'F9': 'F7', 'F6': 'F5'}

{'C2': 'C1', 'C1': 'C2'}

high quality

{'C1': 'low_quality', 'C2': 'high quality'}

LogisticRegression

C2

Music Concert Attendance

The model's prediction for this test case is C2 with an almost 100% confidence level which implies that the likelihood of it being a different class label is closer to 0%. Among the top influential feature-set, F10 has a value shifting the label choice in favour of C1, while the others, F2, F20, and F14, all have a positive impact supporting the decision made by the model to assign the label C2. Other features with positive support or impact on the prediction made include F8, F3, F18, and F15. However, F17, F6, F19, and F4 are the other negatives shifting the prediction decision in the direction of the alternative class label. TO sum up, the positive features clearly outweigh the negative features interms of their contributions, hence the confidence level in the classification output.

{'C2': '98.44%', 'C1': '1.56%'}

{'F2': 'X6', 'F20': 'X11', 'F10': 'X1', 'F14': 'X13', 'F15': 'X2', 'F8': 'X8', 'F17': 'X10', 'F4': 'X14', 'F6': 'X4', 'F3': 'X3', 'F19': 'X9', 'F18': 'X16', 'F12': 'X18', 'F16': 'X7', 'F1': 'X19', 'F9': 'X5', 'F13': 'X17', 'F11': 'X15', 'F7': 'X12', 'F5': 'X20'}

{'F6': 'F2', 'F11': 'F20', 'F1': 'F10', 'F13': 'F14', 'F2': 'F15', 'F8': 'F8', 'F10': 'F17', 'F14': 'F4', 'F4': 'F6', 'F3': 'F3', 'F9': 'F19', 'F16': 'F18', 'F18': 'F12', 'F7': 'F16', 'F19': 'F1', 'F5': 'F9', 'F17': 'F13', 'F15': 'F11', 'F12': 'F7', 'F20': 'F5'}

{'C2': 'C2', 'C1': 'C1'}

< 10k

{'C2': '< 10k', 'C1': '> 10k'}

MLPClassifier

C1

Ethereum Fraud Detection

The classification verdict for the selected case is C1, and the model is very certain about that considering the prediction probabilities across the possible classes. The top variables influencing this decision are F4, F19, F34, F5, and F10. Other variables that are regarded as somewhat important are F37, F20, F29, F11, F31, F21, F16, F22, F28, F24, F15, F8, F35, F38, and F1. Among the top variables, F4 and F19 decrease the prediction response; therefore, they are pushing the verdict toward C2. Similar to these features, F37, F20, and F31 negatively support assigning C1 to the case. Positively supporting the predicted label are the features F34, F5, F10, and F29. Unlike all the features mentioned above, the values of the remaining features such as F7, F23, F9, and F14, are unessential when determining the correct label for this case.

{'C1': '100.00%', 'C2': '0.00%'}

{'F4': 'Unique Received From Addresses', 'F19': ' ERC20 total Ether sent contract', 'F34': 'total ether received', 'F5': 'Number of Created Contracts', 'F10': 'Sent tnx', 'F37': ' ERC20 uniq rec token name', 'F20': ' ERC20 uniq rec contract addr', 'F29': 'max value received ', 'F11': 'total transactions (including tnx to create contract', 'F31': ' ERC20 uniq sent addr.1', 'F21': ' ERC20 uniq sent addr', 'F16': 'Received Tnx', 'F22': ' ERC20 uniq rec addr', 'F28': 'avg val sent', 'F24': 'min value received', 'F15': 'Unique Sent To Addresses', 'F8': ' ERC20 uniq sent token name', 'F35': ' Total ERC20 tnxs', 'F1': 'Time Diff between first and last (Mins)', 'F38': 'Avg min between received tnx', 'F7': 'total Ether sent', 'F23': 'min val sent', 'F9': 'avg val received', 'F14': ' ERC20 avg val sent', 'F2': ' ERC20 max val sent', 'F6': ' ERC20 min val sent', 'F25': ' ERC20 avg val rec', 'F17': ' ERC20 max val rec', 'F27': ' ERC20 min val rec', 'F3': 'max val sent', 'F33': 'min value sent to contract', 'F26': 'max val sent to contract', 'F32': ' ERC20 total ether sent', 'F36': ' ERC20 total Ether received', 'F12': 'avg value sent to contract', 'F18': 'total ether balance', 'F30': 'total ether sent contracts', 'F13': 'Avg min between sent tnx'}

{'F7': 'F4', 'F26': 'F19', 'F20': 'F34', 'F6': 'F5', 'F4': 'F10', 'F38': 'F37', 'F30': 'F20', 'F10': 'F29', 'F18': 'F11', 'F29': 'F31', 'F27': 'F21', 'F5': 'F16', 'F28': 'F22', 'F14': 'F28', 'F9': 'F24', 'F8': 'F15', 'F37': 'F8', 'F23': 'F35', 'F3': 'F1', 'F2': 'F38', 'F19': 'F7', 'F12': 'F23', 'F11': 'F9', 'F36': 'F14', 'F35': 'F2', 'F34': 'F6', 'F33': 'F25', 'F32': 'F17', 'F31': 'F27', 'F13': 'F3', 'F15': 'F33', 'F16': 'F26', 'F25': 'F32', 'F24': 'F36', 'F17': 'F12', 'F22': 'F18', 'F21': 'F30', 'F1': 'F13'}

{'C1': 'C1', 'C2': 'C2'}

Not Fraud

{'C1': 'Not Fraud', 'C2': 'Fraud'}

LogisticRegression

C1

Employee Promotion Prediction

Classifying the given case based on the values of its features, C1 is the best label for the given case since its prediction probability is 99.45%, while C2's is just 0.55 percent. The most relevant factors for the classification or prediction declaration above are F5, F4, and F8, whereas the least influential factors are F3, F1, F6, and F9. The other factors' influence can be described as modest and after further inspecting the direction of effect of the factors, F5, F4, F7, F6, and F9 all contribute positively to giving the label C1. These are the favourable factors that raise the likelihood of C1 being the correct designation, however, F8, F11, and F2 are mostly responsible for minimising the chances of C1 and promoting C2.

{'C2': '0.55%', 'C1': '99.45%'}

{'F5': 'avg_training_score', 'F4': 'KPIs_met >80%', 'F8': 'department', 'F11': 'age', 'F7': 'no_of_trainings', 'F2': 'recruitment_channel', 'F10': 'previous_year_rating', 'F3': 'length_of_service', 'F1': 'education', 'F6': 'region', 'F9': 'gender'}

{'F11': 'F5', 'F10': 'F4', 'F1': 'F8', 'F7': 'F11', 'F6': 'F7', 'F5': 'F2', 'F8': 'F10', 'F9': 'F3', 'F3': 'F1', 'F2': 'F6', 'F4': 'F9'}

{'C2': 'C2', 'C1': 'C1'}

Promote

{'C2': 'Ignore', 'C1': 'Promote'}

LogisticRegression

C2

Concrete Strength Classification

Per the predicted likelihoods across the classes, the model predicts label C2 in this case with a high confidence level. Features F3, F4, F7, and F8 are all driving the model towards the C2 classification, with feature F3 being the strongest driver and F8 being the weak driver among the above mentioned set of features. Features F5 and F6 have moderate negative impact on the C2 classification, while feature F1 has a strong positive impact. Finally, feature F2 has a very weak negative impact on the C2 classification decision driving the model towards assigning C1 to the case here.

{'C2': '90.65%', 'C1': '9.35%'}

{'F3': 'water', 'F4': 'cement', 'F7': 'age_days', 'F8': 'flyash', 'F1': 'superplasticizer', 'F5': 'coarseaggregate', 'F6': 'fineaggregate', 'F2': 'slag'}

{'F4': 'F3', 'F1': 'F4', 'F8': 'F7', 'F3': 'F8', 'F5': 'F1', 'F6': 'F5', 'F7': 'F6', 'F2': 'F2'}

{'C2': 'C2', 'C1': 'C1'}

Weak

{'C2': 'Weak', 'C1': 'Strong'}

KNeighborsClassifier

C1

E-Commerce Shipping

The classifier is very uncertain about the correct class for this example and this is because both classes are shown to be equally likely. The above prediction conclusion is mainly based on the influence of the top input features F6, F10, and F5, while F1, F2, and F3 have less influence on the classifier when classifying the given case. When the direction of influence or contribution of each input feature is examined, only F10, F10, F1, and F3 are revealed to have a positive contribution, improving the classifier's affinity to produce the label C1. The remaining features, F5, F6, F4, F7, F8, and F2 have a negative influence and contribution to the final decision.

{'C1': '50.00%', 'C2': '50.00%'}

{'F6': 'Discount_offered', 'F10': 'Weight_in_gms', 'F5': 'Prior_purchases', 'F4': 'Customer_care_calls', 'F7': 'Product_importance', 'F8': 'Mode_of_Shipment', 'F9': 'Warehouse_block', 'F1': 'Cost_of_the_Product', 'F2': 'Customer_rating', 'F3': 'Gender'}

{'F2': 'F6', 'F3': 'F10', 'F8': 'F5', 'F6': 'F4', 'F9': 'F7', 'F5': 'F8', 'F4': 'F9', 'F1': 'F1', 'F7': 'F2', 'F10': 'F3'}

{'C1': 'C1', 'C2': 'C2'}

On-time

{'C1': 'On-time', 'C2': 'Late'}

LogisticRegression

C2

Airline Passenger Satisfaction

C2 is the label assigned to this data instance based on the fact that C1 is shown to be very unlikely, with a prediction probability of only 0.68%. The variables most relevant to increasing the probability of the prediction here are F13, F7, F14, and F2. Other positive features that increase the chances of predicting C2 are F12, F6, and F21, however, unlike F14, F7, F13, and F2, these have only moderate contributions to the model's classification decision for this instance. In contrast, F17 is the only top-ranked feature that led the model to classify towards C1, while other negative features with a moderately low contribution included F9, F18, F11, and F19. The least relevant features are F22, F5, F1, and F16, with a very low influence on the C2 prediction, however, unlike these features, F8 and F3 are shown to have no impact, since their attributions are very close to zero, when determining the correct label for the case under consideration. Finally, F3 and F8, according to the attribution analysis have no impact on the classification decision here.

{'C2': '99.32%', 'C1': '0.68%'}

{'F14': 'Type of Travel', 'F17': 'Customer Type', 'F7': 'Inflight entertainment', 'F2': 'Inflight wifi service', 'F13': 'Departure\\/Arrival time convenient', 'F11': 'Gate location', 'F19': 'Arrival Delay in Minutes', 'F9': 'Seat comfort', 'F18': 'Online boarding', 'F12': 'Ease of Online booking', 'F6': 'Class', 'F21': 'Age', 'F20': 'On-board service', 'F15': 'Cleanliness', 'F10': 'Checkin service', 'F4': 'Inflight service', 'F22': 'Food and drink', 'F5': 'Departure Delay in Minutes', 'F1': 'Baggage handling', 'F16': 'Gender', 'F8': 'Flight Distance', 'F3': 'Leg room service'}

{'F4': 'F14', 'F2': 'F17', 'F14': 'F7', 'F7': 'F2', 'F8': 'F13', 'F10': 'F11', 'F22': 'F19', 'F13': 'F9', 'F12': 'F18', 'F9': 'F12', 'F5': 'F6', 'F3': 'F21', 'F15': 'F20', 'F20': 'F15', 'F18': 'F10', 'F19': 'F4', 'F11': 'F22', 'F21': 'F5', 'F17': 'F1', 'F1': 'F16', 'F6': 'F8', 'F16': 'F3'}

{'C1': 'C2', 'C2': 'C1'}

neutral or dissatisfied

{'C2': 'neutral or dissatisfied', 'C1': 'satisfied'}

BernoulliNB

C2

Customer Churn Modelling

C2 is the class assigned to this case or instance. However, according to the classifier, there is a 5.75% chance that the other label, C1, is the correct one. The labelling decision above is mainly due to the values F1, F5, and F7. F6 and F2 are the least ranked features since they have marginal attributions. F5, F4, F8, and F1 have values, increasing the odds of C2 being the correct label and these four features are commonly known as positive variables given that they support the classifier's output decision for the given case. The remaining variables had negative attributions, driving the classification decision towards label C1 and the most negative variables are F7, F3, and F9.

{'C2': '94.25%', 'C1': '5.75%'}

{'F1': 'IsActiveMember', 'F5': 'NumOfProducts', 'F7': 'Gender', 'F3': 'Geography', 'F9': 'Age', 'F10': 'CreditScore', 'F4': 'EstimatedSalary', 'F8': 'Balance', 'F6': 'HasCrCard', 'F2': 'Tenure'}

{'F9': 'F1', 'F7': 'F5', 'F3': 'F7', 'F2': 'F3', 'F4': 'F9', 'F1': 'F10', 'F10': 'F4', 'F6': 'F8', 'F8': 'F6', 'F5': 'F2'}

{'C1': 'C2', 'C2': 'C1'}

Stay

{'C2': 'Stay', 'C1': 'Leave'}

LogisticRegression

C2

Real Estate Investment

The model predicts the class label of this test case or instance as C2 and it is quite confident in the above prediction decision considering the predicted confidence level. The above prediction decision was made primarily based on the values of the following features: F20, F6, F14, and F16. The top features, F20 and F6, positively contribute to the final prediction of C2. Besides, F16 also has a positive impact, pushing the model to output C2. However, the value of F14 supports the prediction of the alternative label, C1. However, compared to F20 and F6, the influence of F14 is very small. The features with moderate influence or impact on the prediction made for this test case are F1, F15, and F10. While F1 moderately supports the C2 prediction, F15 and F10 have values, pushing the model toward predicting C1.

{'C1': '2.40%', 'C2': '97.60%'}

{'F20': 'Feature7', 'F6': 'Feature4', 'F14': 'Feature2', 'F16': 'Feature14', 'F12': 'Feature15', 'F13': 'Feature8', 'F1': 'Feature20', 'F15': 'Feature1', 'F10': 'Feature17', 'F5': 'Feature3', 'F2': 'Feature16', 'F9': 'Feature18', 'F3': 'Feature10', 'F8': 'Feature5', 'F7': 'Feature6', 'F18': 'Feature12', 'F19': 'Feature19', 'F4': 'Feature13', 'F11': 'Feature9', 'F17': 'Feature11'}

{'F11': 'F20', 'F9': 'F6', 'F1': 'F14', 'F17': 'F16', 'F4': 'F12', 'F3': 'F13', 'F20': 'F1', 'F7': 'F15', 'F6': 'F10', 'F8': 'F5', 'F18': 'F2', 'F19': 'F9', 'F13': 'F3', 'F2': 'F8', 'F10': 'F7', 'F15': 'F18', 'F5': 'F19', 'F16': 'F4', 'F12': 'F11', 'F14': 'F17'}

{'C1': 'C1', 'C2': 'C2'}

Invest

{'C1': 'Ignore', 'C2': 'Invest'}

DecisionTreeClassifier

C2

Concrete Strength Classification

The case is labelled as C2 by the classification model, and according to the model, there is little to no chance that the correct label could be C1. Per the feature attribution inspection, F3 and F6 are the least influential features. The classification decision to label this case as C2 is mainly due to the positive contributions of F8, F4, and F1. However, the strong negative influence of F5 indicates that the true label could be C1, but since the likelihood of C1 is 0.0%, we can say that the positive features successfully drive the decision in favour of the C2 label. F7, F2, and F6 are the other negative features that unsuccessfully attempt to shift the decision in favour of C1. From the attribution analysis and the predicted likelihoods across the classes, we can conclude that the model is certain that C1 is not the true label.

{'C1': '0.00%', 'C2': '100.00%'}

{'F5': 'cement', 'F8': 'age_days', 'F4': 'water', 'F1': 'superplasticizer', 'F7': 'coarseaggregate', 'F2': 'fineaggregate', 'F3': 'flyash', 'F6': 'slag'}

{'F1': 'F5', 'F8': 'F8', 'F4': 'F4', 'F5': 'F1', 'F6': 'F7', 'F7': 'F2', 'F3': 'F3', 'F2': 'F6'}

{'C1': 'C1', 'C2': 'C2'}

Strong

{'C1': 'Weak', 'C2': 'Strong'}

SVC

C1

Real Estate Investment

The decision of the classification model on the true label with respect to the given case is based on the information provided to it. From the prediction probabilities, C1 is selected by the model as the most likely label, with a very high confidence level equal to 97.49%. According to the attributions analysis, the very high confidence in the validity of C1 can be attributed to the very strong positive influence of F14, F4, and F19. The contributions of all the other features are moderate to low. The least relevant features are F16, F1, F13, and F11, whereas the moderate ones include F17, F10, F9, and F20. The very marginal uncertainty with respect to the classification decision here can be blamed on the moderate influence of negative features such as F17, F10, F9, F2, F18, and F20. Aside from F14, F4, and F19, some of the other positive features are F15, F12, and F1, with moderate to low contributions, pushing the decision further higher towards C1 away from C2. Finally, F11 has a negligible contribution to the decision above.

{'C2': '2.51%', 'C1': '97.49%'}

{'F14': 'Feature7', 'F4': 'Feature4', 'F19': 'Feature14', 'F17': 'Feature2', 'F10': 'Feature3', 'F9': 'Feature8', 'F20': 'Feature13', 'F2': 'Feature15', 'F18': 'Feature1', 'F6': 'Feature11', 'F3': 'Feature9', 'F5': 'Feature16', 'F15': 'Feature12', 'F7': 'Feature18', 'F12': 'Feature19', 'F8': 'Feature5', 'F16': 'Feature6', 'F1': 'Feature10', 'F13': 'Feature20', 'F11': 'Feature17'}

{'F11': 'F14', 'F9': 'F4', 'F17': 'F19', 'F1': 'F17', 'F8': 'F10', 'F3': 'F9', 'F16': 'F20', 'F4': 'F2', 'F7': 'F18', 'F14': 'F6', 'F12': 'F3', 'F18': 'F5', 'F15': 'F15', 'F19': 'F7', 'F5': 'F12', 'F2': 'F8', 'F10': 'F16', 'F13': 'F1', 'F20': 'F13', 'F6': 'F11'}

{'C2': 'C2', 'C1': 'C1'}

Invest

{'C2': 'Ignore', 'C1': 'Invest'}

KNeighborsClassifier

C3

Cab Surge Pricing System

With a moderate likelihood of 50.0%, the label for this case is judged to be C3. The classifier, on the other hand, says that C2 and C1 are equally likely, with a predicted probability of 25.0 percent. The aforementioned decision is mostly dependent on the features of the given case and the values of F2, F4, and F8 are demonstrated to be the primary factors influencing the classification output decision. When compared to F2, F4, and F8, the other variables, such as F5, F3, and F11, have lower attributions. According to the attribution assessment, F2, F4, F8, F3, and F9 are the factors that positively contribute to the choice, implying that they are the ones that push the classification closer towards C3. F5, F11, F10, F7, and F12, on the other hand, are the top negative factors that sway the choice somewhat toward the other labels, C2 and C1. In fact, it is because of these negative variables that the classifier presents the probabilities across the C1 and C2.

{'C2': '25.00%', 'C1': '25.00%', 'C3': '50.00%'}

{'F2': 'Destination_Type', 'F4': 'Cancellation_Last_1Month', 'F8': 'Trip_Distance', 'F5': 'Customer_Rating', 'F3': 'Var1', 'F11': 'Life_Style_Index', 'F10': 'Confidence_Life_Style_Index', 'F7': 'Var3', 'F9': 'Customer_Since_Months', 'F12': 'Gender', 'F6': 'Var2', 'F1': 'Type_of_Cab'}

{'F6': 'F2', 'F8': 'F4', 'F1': 'F8', 'F7': 'F5', 'F9': 'F3', 'F4': 'F11', 'F5': 'F10', 'F11': 'F7', 'F3': 'F9', 'F12': 'F12', 'F10': 'F6', 'F2': 'F1'}

{'C1': 'C2', 'C2': 'C1', 'C3': 'C3'}

C3

{'C2': 'Low', 'C1': 'Medium', 'C3': 'High'}

LogisticRegression

C2

Music Concert Attendance

With a prediction probability of around 82.06 percent, the algorithm predicts class C2. In the aforementioned prediction judgment, F7, F2, F15, and F19 are all important. The top positively contributing features supporting the C2 prediction are F7, F2, and F19, while F15 is pushing the final prediction away. F16 also has a positive impact on the categorization, but F6 has a negative impact and finally, F3, F11, F14, and F20 have very little influence on the algorithm among the features, when picking the most appropriate label in this case.

{'C1': '17.94%', 'C2': '82.06%'}

{'F7': 'X11', 'F2': 'X1', 'F15': 'X13', 'F19': 'X3', 'F6': 'X8', 'F16': 'X6', 'F9': 'X2', 'F10': 'X9', 'F17': 'X17', 'F18': 'X10', 'F12': 'X4', 'F1': 'X14', 'F5': 'X20', 'F8': 'X18', 'F13': 'X19', 'F4': 'X7', 'F20': 'X12', 'F3': 'X15', 'F14': 'X16', 'F11': 'X5'}

{'F11': 'F7', 'F1': 'F2', 'F13': 'F15', 'F3': 'F19', 'F8': 'F6', 'F6': 'F16', 'F2': 'F9', 'F9': 'F10', 'F17': 'F17', 'F10': 'F18', 'F4': 'F12', 'F14': 'F1', 'F20': 'F5', 'F18': 'F8', 'F19': 'F13', 'F7': 'F4', 'F12': 'F20', 'F15': 'F3', 'F16': 'F14', 'F5': 'F11'}

{'C2': 'C1', 'C1': 'C2'}

> 10k

{'C1': '< 10k', 'C2': '> 10k'}

LogisticRegression

C1

House Price Classification

For this test case, the model predicts C1 with 99.93% certainty and what this means is that there is only 0.07% chance that C2 could be the right one. The features with the highest impact are F10, F7, F8, and F1, which are all shown to contribute positively to the prediction decision mentioned above. While F13 and F12 support the prediction, F9 is the feature with the strongest negative support for the prediction. Of the features with a small impact, namely F11, F4, F2, F6, F3, and F5, only F4 and F6 negatively support the prediction while the others positively support it.

{'C2': '0.07%', 'C1': '99.93%'}

{'F10': 'LSTAT', 'F7': 'RM', 'F8': 'PTRATIO', 'F1': 'RAD', 'F9': 'CHAS', 'F13': 'TAX', 'F12': 'CRIM', 'F11': 'DIS', 'F4': 'AGE', 'F2': 'B', 'F6': 'ZN', 'F3': 'NOX', 'F5': 'INDUS'}

{'F13': 'F10', 'F6': 'F7', 'F11': 'F8', 'F9': 'F1', 'F4': 'F9', 'F10': 'F13', 'F1': 'F12', 'F8': 'F11', 'F7': 'F4', 'F12': 'F2', 'F2': 'F6', 'F5': 'F3', 'F3': 'F5'}

{'C1': 'C2', 'C2': 'C1'}

High

{'C2': 'Low', 'C1': 'High'}

BernoulliNB

C1

Employee Promotion Prediction

This model trained on eleven attributes predicts class label C1 for this case with a confidence level equal to 54.21%. This suggests that the likelihood of C2 being the correct label is 45.79%. The classification decision above is mainly based on the influence of the features F4, F10, F9, and F2. The most relevant features are the negative features, F4, F10, and F9. These features are regarded as negative features given that their values are shifting the prediction decision in the direction of C2. The positive attributes are F2, F7, F3, F11, and F8, supporting the model's prediction for this case.

{'C1': '54.21%', 'C2': '45.79%'}

{'F4': 'KPIs_met >80%', 'F10': 'previous_year_rating', 'F9': 'avg_training_score', 'F2': 'department', 'F7': 'education', 'F3': 'recruitment_channel', 'F6': 'no_of_trainings', 'F5': 'length_of_service', 'F11': 'region', 'F1': 'age', 'F8': 'gender'}

{'F10': 'F4', 'F8': 'F10', 'F11': 'F9', 'F1': 'F2', 'F3': 'F7', 'F5': 'F3', 'F6': 'F6', 'F9': 'F5', 'F2': 'F11', 'F7': 'F1', 'F4': 'F8'}

{'C2': 'C1', 'C1': 'C2'}

Ignore

{'C1': 'Ignore', 'C2': 'Promote'}

LogisticRegression

C1

Used Cars Price-Range Prediction

With a moderate confidence level of 67.95%, the model predicts C1 for the case under consideration, but it is important to consider the fact that there is a 32.05% chance that C2 could be the correct label instead. The most influential variables resulting in the aforementioned classification decision are F4, F7, and F6. While F4 and F7 have negative contributions towards the C1 prediction; favouring the assignment of C2 instead, F6 is the top positive contributing feature. F9, F2, and F5 had a small positive effect on prediction, whereas F8 had a smaller negative effect. Finally, F3 is the least relevant variable, and therefore, its negative attribution has no significant influence on the model with respect to the given case.

{'C2': '32.05%', 'C1': '67.95%'}

{'F4': 'Fuel_Type', 'F7': 'Seats', 'F6': 'car_age', 'F10': 'Name', 'F1': 'Owner_Type', 'F9': 'Power', 'F5': 'Engine', 'F8': 'Transmission', 'F2': 'Mileage', 'F3': 'Kilometers_Driven'}

{'F7': 'F4', 'F10': 'F7', 'F5': 'F6', 'F6': 'F10', 'F9': 'F1', 'F4': 'F9', 'F3': 'F5', 'F8': 'F8', 'F2': 'F2', 'F1': 'F3'}

{'C1': 'C2', 'C2': 'C1'}

High

{'C2': 'Low', 'C1': 'High'}

LogisticRegression

C2

Employee Promotion Prediction

As per the classification algorithm, the most appropriate label for the given case is C2 because its prediction likelihood is 99.45%, whereas that of C1 is only 0.55%. For the classification or prediction assertion above, the most important variables are F4, F1, and F5, while the least influential variables are F2, F8, F7, and F3. Regarding the direction of influence of the variables, the ones with positive contributions to assigning label C2 are F4, F1, F6, F7, and F3 which in fact increase the odds of C2 being the correct label. Finally, decreasing the odds of C2 and supporting C1 are mainly the values of the variables F5, F11, and F10.

{'C1': '0.55%', 'C2': '99.45%'}

{'F4': 'avg_training_score', 'F1': 'KPIs_met >80%', 'F5': 'department', 'F11': 'age', 'F6': 'no_of_trainings', 'F10': 'recruitment_channel', 'F9': 'previous_year_rating', 'F2': 'length_of_service', 'F8': 'education', 'F7': 'region', 'F3': 'gender'}

{'F11': 'F4', 'F10': 'F1', 'F1': 'F5', 'F7': 'F11', 'F6': 'F6', 'F5': 'F10', 'F8': 'F9', 'F9': 'F2', 'F3': 'F8', 'F2': 'F7', 'F4': 'F3'}

{'C1': 'C1', 'C2': 'C2'}

Promote

{'C1': 'Ignore', 'C2': 'Promote'}

SGDClassifier

C1

House Price Classification

C1 is the label predicted by the classification model employed and looking at the prediction probabilities, it valid to concluded that the model is very certain about the selected label. The features considered most relevant by the model for the above decision are F6, F1, F13, and F12, while those with the least consideration are F4, F10, and F7. On the basis of the analysis, majority of the input features positively affirm the prediction for this case; therefore, it is not surprising that the model chose the C1 label and the positive features include F6, F13, F12, F11, F3, F8, and F2. The three negative features that moderately bias the labelling decision towards C2 are F5, F1, and F9.

{'C1': '100.00%', 'C2': '0.00%'}

{'F6': 'CRIM', 'F13': 'LSTAT', 'F1': 'RAD', 'F12': 'AGE', 'F3': 'CHAS', 'F9': 'DIS', 'F11': 'ZN', 'F8': 'TAX', 'F2': 'PTRATIO', 'F5': 'B', 'F4': 'RM', 'F10': 'NOX', 'F7': 'INDUS'}

{'F1': 'F6', 'F13': 'F13', 'F9': 'F1', 'F7': 'F12', 'F4': 'F3', 'F8': 'F9', 'F2': 'F11', 'F10': 'F8', 'F11': 'F2', 'F12': 'F5', 'F6': 'F4', 'F5': 'F10', 'F3': 'F7'}

{'C2': 'C1', 'C1': 'C2'}

Low

{'C1': 'Low', 'C2': 'High'}

LogisticRegression

C2

E-Commerce Shipping

The confidence level for the prediction made for the given case is 71.57%. F10 has a significant impact on the outcome in the negative. The values F9, F7, F6, F4, F2, F3, and F5 all have a positive impact on the results, but they are still less than the effects of F10. The analysis shows that F10 has the highest impact on the model's prediction decision here, it has an overwhelmingly negative effect. F7, F6, F4, and F2 have a positive effect on the model's prediction. Because of the strength of the F10 feature, all other features have little effect on the outcome. In addition, the uncertainty in the prediction could be attributed to the pull of F10, which drives the model to predict an alternative label.

{'C2': '71.57%', 'C1': '28.43%'}

{'F10': 'Discount_offered', 'F9': 'Weight_in_gms', 'F7': 'Prior_purchases', 'F6': 'Product_importance', 'F4': 'Cost_of_the_Product', 'F2': 'Gender', 'F3': 'Customer_rating', 'F5': 'Warehouse_block', 'F1': 'Customer_care_calls', 'F8': 'Mode_of_Shipment'}

{'F2': 'F10', 'F3': 'F9', 'F8': 'F7', 'F9': 'F6', 'F1': 'F4', 'F10': 'F2', 'F7': 'F3', 'F4': 'F5', 'F6': 'F1', 'F5': 'F8'}

{'C2': 'C2', 'C1': 'C1'}

On-time

{'C2': 'On-time', 'C1': 'Late'}

LogisticRegression

C2

E-Commerce Shipping

53.78% and 46.22%, respectively, are the chance or likelihood of any of the classes C2, and C1 being the appropriate label for the case given here. As a result, it's safe to say that C2 is the most likely label for this situation and F2 is identified as the most influential feature whereas F3, F4, and F6 have very low contributions to the decision made by the classification algorithm with respect to the given case. In addition, F8, F10, F9, F1, F5, and F7 have moderate contributions higher than F3, F4, and F6 but lower than F2. Despite the strong positive influence of F2 and F10 supporting the assignment of C2, the negative influence of F8, F9, F1, F7, and F6 shift the classification judgment fairly towards the C1 label which explains the 46.22% likelihood.

{'C1': '46.22%', 'C2': '53.78%'}

{'F2': 'Discount_offered', 'F8': 'Weight_in_gms', 'F10': 'Prior_purchases', 'F9': 'Product_importance', 'F1': 'Cost_of_the_Product', 'F5': 'Gender', 'F7': 'Customer_rating', 'F3': 'Customer_care_calls', 'F4': 'Mode_of_Shipment', 'F6': 'Warehouse_block'}

{'F2': 'F2', 'F3': 'F8', 'F8': 'F10', 'F9': 'F9', 'F1': 'F1', 'F10': 'F5', 'F7': 'F7', 'F6': 'F3', 'F5': 'F4', 'F4': 'F6'}

{'C1': 'C1', 'C2': 'C2'}

Late

{'C1': 'On-time', 'C2': 'Late'}

LogisticRegression

C2

Used Cars Price-Range Prediction

The output decision for the provided data is C2, with a very high confidence level, based on the output prediction probabilities across the two classes since C1 has a probability of around 0.00%. F1, F3, and F9 are the most influential factors in the above-mentioned label assignment, however F4 and F7 are the least influential. The unusually high degree of confidence associated with the classification choice in this case might be attributable to the fact that the bulk of the input variables exhibit attributions that improve the model's responsiveness towards label C2. F8, F5, and F4 have only the negative contributions, attempting to persuade the model to classify this case as C1. To cut a long story short, the joint contribution of the negative variables is quite low in comparison to that of the positive variables, resulting in the model's certainty in the decision above.

{'C2': '100.00%', 'C1': '0.00%'}

{'F3': 'car_age', 'F1': 'Power', 'F9': 'Fuel_Type', 'F6': 'Engine', 'F2': 'Seats', 'F10': 'Transmission', 'F8': 'Kilometers_Driven', 'F5': 'Name', 'F4': 'Mileage', 'F7': 'Owner_Type'}

{'F5': 'F3', 'F4': 'F1', 'F7': 'F9', 'F3': 'F6', 'F10': 'F2', 'F8': 'F10', 'F1': 'F8', 'F6': 'F5', 'F2': 'F4', 'F9': 'F7'}

{'C1': 'C2', 'C2': 'C1'}

Low

{'C2': 'Low', 'C1': 'High'}

SVC

C1

Advertisement Prediction

When given the task of labelling the given case one of the possible labels, C1 and C2, the model assigns C1 as the most likely correct label, with a confidence level of roughly 99.90%. This degree of confidence indicates that the likelihood of C2 being the right designation is merely 0.10%. According to the attribution analysis, each variable has a distinct degree of effect or contribution to the model's arriving at the above-mentioned classification. F2, F4, F5, and F7 are the features accounting for the model's extremely high confidence in the assigned label. In fact, the only input variables having a negative impact are also the least relevant ones, F1 and F3.

{'C1': '99.90%', 'C2': '0.10%'}

{'F4': 'Daily Internet Usage', 'F2': 'Daily Time Spent on Site', 'F7': 'Age', 'F5': 'ad_day', 'F6': 'Area Income', 'F3': 'Gender', 'F1': 'ad_month'}

{'F4': 'F4', 'F1': 'F2', 'F2': 'F7', 'F7': 'F5', 'F3': 'F6', 'F5': 'F3', 'F6': 'F1'}

{'C2': 'C1', 'C1': 'C2'}

Skip

{'C1': 'Skip', 'C2': 'Watch'}

LogisticRegression

C1

Concrete Strength Classification

According to the classification model employed here, the most probable label for the given case is C1 with a confidence level equal to 98.97%. Per the attributions analysis, F2 and F8 are the most significant and influential features driving label selection. The least ranked features are F7 and F4, while F1, F6, F3, and F5 have moderate contributions. Negatively supporting the above classification output are F8, F3, and F5, pushing the model to assign the alternative label. However, given the fact that the prediction probability of C2 is only 1.03%, it can be concluded that the joint positive influence of F2, F1, F6, F7, and F4 strongly drives the model to label the case as C1 instead of C2.

{'C2': '1.03%', 'C1': '98.97%'}

{'F2': 'cement', 'F8': 'age_days', 'F1': 'water', 'F6': 'superplasticizer', 'F3': 'fineaggregate', 'F5': 'flyash', 'F7': 'slag', 'F4': 'coarseaggregate'}

{'F1': 'F2', 'F8': 'F8', 'F4': 'F1', 'F5': 'F6', 'F7': 'F3', 'F3': 'F5', 'F2': 'F7', 'F6': 'F4'}

{'C1': 'C2', 'C2': 'C1'}

Strong

{'C2': 'Weak', 'C1': 'Strong'}

KNeighborsClassifier

C2

Wine Quality Prediction

The classifier is quite sure that the right label for the data given is C2 based on the influence of variables such as F7, F4, F1, and F9. There is a 10.0% chance that the correct label is C1 and per the attributions examination conducted, the bulk of the traits contribute positively, with only three contributing negatively. The negative variables are F9, F6, and F5, which reduce the classifier's preference for C2. F7, F4, and F1 are notable positive variables that boost the classifier's response to outputting C2 rather than C1. All in all, the classifier's confidence in this prediction may be attributed to the fact that the negative variables only have a minor influence on the prediction choice here.

{'C1': '10.00%', 'C2': '90.00%'}

{'F7': 'sulphates', 'F4': 'total sulfur dioxide', 'F1': 'volatile acidity', 'F9': 'residual sugar', 'F8': 'citric acid', 'F10': 'chlorides', 'F3': 'alcohol', 'F2': 'fixed acidity', 'F6': 'density', 'F11': 'pH', 'F5': 'free sulfur dioxide'}

{'F10': 'F7', 'F7': 'F4', 'F2': 'F1', 'F4': 'F9', 'F3': 'F8', 'F5': 'F10', 'F11': 'F3', 'F1': 'F2', 'F8': 'F6', 'F9': 'F11', 'F6': 'F5'}

{'C1': 'C1', 'C2': 'C2'}

high quality

{'C1': 'low_quality', 'C2': 'high quality'}

DecisionTreeClassifier

C2

Vehicle Insurance Claims

C2 was assigned to the given case by the classifier with a likelihood of 93.32%, leaving thhe likelihood of the C1 equal to only 6.68%. The most influential features were F26, F12, and F4. The remaining features with non-zero attributions are F33, F21, F10, F15, F16, F18, F7, F8, F22, F13, F14, F28, F5, F20, F11, and finally F31. F26 and F12 were highly influential in the positive direction, increasing the odds of the predicted label being correct, whereas F4 had a negative impact, driving the prediction in favour of a different label. Furthermore, F33 had a positive impact on the prediction, whereas F21 and F10 negatively influenced the prediction. Finally, the features that we can say have no impact at all on the prediction made here are as follows: F19, F3, F23, F30, and F6.

{'C2': '93.32%', 'C1': '6.68%'}

{'F26': 'incident_severity', 'F12': 'incident_city', 'F4': 'injury_claim', 'F33': 'insured_occupation', 'F21': 'insured_zip', 'F10': 'authorities_contacted', 'F15': 'auto_year', 'F16': 'police_report_available', 'F18': 'bodily_injuries', 'F7': 'insured_hobbies', 'F8': 'insured_sex', 'F22': 'auto_make', 'F13': 'property_damage', 'F14': 'witnesses', 'F28': 'insured_relationship', 'F29': 'age', 'F20': 'vehicle_claim', 'F5': 'months_as_customer', 'F11': 'property_claim', 'F31': 'incident_type', 'F19': 'capital-gains', 'F3': 'policy_deductable', 'F23': 'policy_annual_premium', 'F30': 'incident_state', 'F6': 'umbrella_limit', 'F9': 'total_claim_amount', 'F32': 'collision_type', 'F27': 'incident_hour_of_the_day', 'F2': 'insured_education_level', 'F24': 'number_of_vehicles_involved', 'F25': 'policy_csl', 'F1': 'policy_state', 'F17': 'capital-loss'}

{'F27': 'F26', 'F30': 'F12', 'F14': 'F4', 'F22': 'F33', 'F6': 'F21', 'F28': 'F10', 'F17': 'F15', 'F32': 'F16', 'F11': 'F18', 'F23': 'F7', 'F20': 'F8', 'F33': 'F22', 'F31': 'F13', 'F12': 'F14', 'F24': 'F28', 'F2': 'F29', 'F16': 'F20', 'F1': 'F5', 'F15': 'F11', 'F25': 'F31', 'F7': 'F19', 'F3': 'F3', 'F4': 'F23', 'F29': 'F30', 'F5': 'F6', 'F13': 'F9', 'F26': 'F32', 'F9': 'F27', 'F21': 'F2', 'F10': 'F24', 'F19': 'F25', 'F18': 'F1', 'F8': 'F17'}

{'C2': 'C2', 'C1': 'C1'}

Not Fraud

{'C2': 'Not Fraud', 'C1': 'Fraud'}

AdaBoostClassifier

C2

Basketball Players Career Length Prediction

With moderately high confidence, the classifier indicates that the most probable label for the given data is C2 with only just a 21.80% chance that it could be C1. The main driving features for the above classification or prediction decision are F7 and F6. The remaining features such as F17, F3, F2, and F14 have moderate to low influence on the above decision. Inspecting the attributions of the the input features showed that the ones with negative impact or contribution are F17, F14, F11, F12, and F15. From the attributions, we can see that the remaining features have positive contributions or influence and as a matter of fact, the certainty of the classifier for this classification can be attributed mainly to the strong positive contributions of F7 and F6 coupled with the contributions of the other positive features such as F3, F2, F13, and F4.

{'C2': '78.20%', 'C1': '21.80%'}

{'F7': 'GamesPlayed', 'F6': 'PointsPerGame', 'F17': 'Steals', 'F3': 'MinutesPlayed', 'F2': 'DefensiveRebounds', 'F14': 'Rebounds', 'F13': 'Blocks', 'F4': 'FreeThrowAttempt', 'F11': 'FieldGoalPercent', 'F16': 'FreeThrowMade', 'F12': 'OffensiveRebounds', 'F5': 'FieldGoalsMade', 'F1': '3PointAttempt', 'F8': 'FreeThrowPercent', 'F19': '3PointMade', 'F9': 'FieldGoalsAttempt', 'F18': 'Turnovers', 'F15': 'Assists', 'F10': '3PointPercent'}

{'F1': 'F7', 'F3': 'F6', 'F17': 'F17', 'F2': 'F3', 'F14': 'F2', 'F15': 'F14', 'F18': 'F13', 'F11': 'F4', 'F6': 'F11', 'F10': 'F16', 'F13': 'F12', 'F4': 'F5', 'F8': 'F1', 'F12': 'F8', 'F7': 'F19', 'F5': 'F9', 'F19': 'F18', 'F16': 'F15', 'F9': 'F10'}

{'C1': 'C2', 'C2': 'C1'}

More than 5

{'C2': 'More than 5', 'C1': 'Less than 5'}

GradientBoostingClassifier

C2

Health Care Services Satisfaction Prediction

Given the fact that the likelihood of C1 being the correct label for the case under consideration is only 36.34%, the model assigns the label C2. The prediction decision between the two classes is highly based on the values of the features F5, F4, F13, and F16, whereas those with the least attributions or contributions regarding this label assignment are F15 and F2. Among the top influential features, F5 and F4 have very strong positive contributions, increasing the probability of the label C2, while the value of F13 value suggests the other label, C1, could be the true label. This pull or shift towards label C1 is further supported by the values of F11, F14, F7, F9, F15, and F1. Conversely, the remaining features, together with F5 and F4, positively encourage the prediction of C2.

{'C2': '63.66%', 'C1': '36.34%'}

{'F5': 'Communication with dr', 'F4': 'Modern equipment', 'F13': 'Specialists avaliable', 'F16': 'Quality\\/experience dr.', 'F11': 'Time waiting', 'F14': 'Admin procedures', 'F7': 'Hygiene and cleaning', 'F9': 'waiting rooms', 'F1': 'avaliablity of drugs', 'F8': 'Time of appointment', 'F10': 'hospital rooms quality', 'F3': 'Exact diagnosis', 'F6': 'parking, playing rooms, caffes', 'F12': 'friendly health care workers', 'F15': 'Check up appointment', 'F2': 'lab services'}

{'F8': 'F5', 'F10': 'F4', 'F7': 'F13', 'F6': 'F16', 'F2': 'F11', 'F3': 'F14', 'F4': 'F7', 'F14': 'F9', 'F13': 'F1', 'F5': 'F8', 'F15': 'F10', 'F9': 'F3', 'F16': 'F6', 'F11': 'F12', 'F1': 'F15', 'F12': 'F2'}

{'C2': 'C2', 'C1': 'C1'}

Dissatisfied

{'C2': 'Dissatisfied', 'C1': 'Satisfied'}

KNeighborsClassifier

C1

Cab Surge Pricing System

C1, out of the three potential classes, is the the label assigned with a high probability of 50.0%. However, the classifier indicates that C3 and C2 are equally likely, with a predicted probability of 25.0%. The aforementioned judgement is mostly based on the variables of the given case. The variables F11, F1, and F2 are shown to be the main factors resulting in the classification output decision. The remaining variables, such as F12, F8, and F10, have lower attributions compared to F11, F1, and F2. The attribution analysis also indicated that F11, F1, F2, F8, and F9 are the variables that positively contribute to the decision, meaning they are the ones that shift the classification higher towards C1. On the contrary, F12, F10, F4, F3, and F6 are the top negative variables that steer the decision slightly towards the other labels, C3 and C2. In fact, it is because of these negative variables that the classifier indicates the probabilities across the C2 and C3.

{'C3': '25.00%', 'C2': '25.00%', 'C1': '50.00%'}

{'F11': 'Destination_Type', 'F1': 'Cancellation_Last_1Month', 'F2': 'Trip_Distance', 'F12': 'Customer_Rating', 'F8': 'Var1', 'F10': 'Life_Style_Index', 'F4': 'Confidence_Life_Style_Index', 'F3': 'Var3', 'F9': 'Customer_Since_Months', 'F6': 'Gender', 'F5': 'Var2', 'F7': 'Type_of_Cab'}

{'F6': 'F11', 'F8': 'F1', 'F1': 'F2', 'F7': 'F12', 'F9': 'F8', 'F4': 'F10', 'F5': 'F4', 'F11': 'F3', 'F3': 'F9', 'F12': 'F6', 'F10': 'F5', 'F2': 'F7'}

{'C2': 'C3', 'C1': 'C2', 'C3': 'C1'}

C3

{'C3': 'Low', 'C2': 'Medium', 'C1': 'High'}

LogisticRegression

C1

Flight Price-Range Classification

The chances of selecting the correct label from one of the possible labels C3, C2, and C1 are 18.51%, 5.86%, and 75.63%, respectively. As a result, it can be deduced that the classifier's anticipated label in this situation is C1. The values of the input features were used as the basis to make the aforementioned prediction judgments. Some of these features have values that positively support the assigned label, while others have values that contradict the classifier's decision, driving it toward one of the other two labels. F9 is the most influential feature, following which are the variables F8, F6, F4, and F11, enumerated according to their respective relevance to the aforementioned label selection. F9, F4, and F11 are positive features that increase the classifier's response towards generating the C1 label, but F8 and F6 are negative features, lowering the odds of C1 being the correct label. F3, F5, F7, F10, and F1 are features that have a moderate influence on the classifier in this case, while F2 and F12 have only a marginal impact. F8, F6, F7, and F12 are the features that have values supporting the assignment of any of the other labels, while the rest favour the C1 prediction, therefore, the predicted probabilities across labels is unsurprising. Furthermore, the predicted likelihood of C1 is higher than all the other labels which is attributed to the fact that the positive features' combined impact is bigger than negative features' combined impact.

{'C2': '5.86%', 'C3': '18.51%', 'C1': '75.63%'}

{'F9': 'Total_Stops', 'F8': 'Airline', 'F6': 'Journey_day', 'F4': 'Source', 'F11': 'Destination', 'F5': 'Journey_month', 'F3': 'Dep_hour', 'F7': 'Arrival_minute', 'F1': 'Arrival_hour', 'F10': 'Duration_hours', 'F2': 'Dep_minute', 'F12': 'Duration_mins'}

{'F12': 'F9', 'F9': 'F8', 'F1': 'F6', 'F10': 'F4', 'F11': 'F11', 'F2': 'F5', 'F3': 'F3', 'F6': 'F7', 'F5': 'F1', 'F7': 'F10', 'F4': 'F2', 'F8': 'F12'}

{'C2': 'C2', 'C1': 'C3', 'C3': 'C1'}

High

{'C2': 'Low', 'C3': 'Moderate', 'C1': 'High'}

GradientBoostingClassifier

C2

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In this case, the model expects C2 to be a label since the probability that the label is the alternative class C1 is only 1.94%. This means that the model has a lot of confidence in the selected label, C2. F21 and F8 are the two most important prediction variables positively controlling the assignment of C2 in this case. Other variables that contributed positively to this prediction included F1, F17, F13, F42, and F39. On the other hand, the values F31, F26, F22, and F9 constitute a feature set with a negative impact on the above prediction decision. However, the above features have little effect on the model compared to the F3, F39, F17, and F8, which may explain why the model is confident that the true label is probably C2. Finally, for the case under consideration, F7, F23, F28, F30, F25, and F33 are some of the features, with practically no effect on the prediction decisions of the model, hence they can be considered negligible to the classification here.

{'C2': '98.06%', 'C1': '1.94%'}

{'F3': 'X38', 'F8': 'X22', 'F39': 'X32', 'F17': 'X19', 'F42': 'X1', 'F1': 'X13', 'F31': 'X11', 'F13': 'X3', 'F26': 'X16', 'F22': 'X2', 'F9': 'X12', 'F12': 'X14', 'F35': 'X42', 'F2': 'X18', 'F38': 'X28', 'F21': 'X35', 'F18': 'X24', 'F32': 'X20', 'F5': 'X8', 'F11': 'X40', 'F7': 'X34', 'F23': 'X5', 'F33': 'X4', 'F28': 'X41', 'F30': 'X6', 'F25': 'X39', 'F6': 'X7', 'F20': 'X37', 'F16': 'X36', 'F14': 'X33', 'F34': 'X21', 'F36': 'X9', 'F40': 'X31', 'F10': 'X30', 'F15': 'X10', 'F4': 'X27', 'F24': 'X26', 'F19': 'X25', 'F27': 'X15', 'F41': 'X23', 'F37': 'X17', 'F29': 'X29'}

{'F35': 'F3', 'F20': 'F8', 'F29': 'F39', 'F17': 'F17', 'F40': 'F42', 'F11': 'F1', 'F9': 'F31', 'F2': 'F13', 'F14': 'F26', 'F1': 'F22', 'F10': 'F9', 'F12': 'F12', 'F38': 'F35', 'F16': 'F2', 'F26': 'F38', 'F32': 'F21', 'F22': 'F18', 'F18': 'F32', 'F6': 'F5', 'F37': 'F11', 'F31': 'F7', 'F41': 'F23', 'F3': 'F33', 'F39': 'F28', 'F4': 'F30', 'F36': 'F25', 'F5': 'F6', 'F34': 'F20', 'F33': 'F16', 'F30': 'F14', 'F19': 'F34', 'F7': 'F36', 'F28': 'F40', 'F27': 'F10', 'F8': 'F15', 'F25': 'F4', 'F24': 'F24', 'F23': 'F19', 'F13': 'F27', 'F21': 'F41', 'F15': 'F37', 'F42': 'F29'}

{'C1': 'C2', 'C2': 'C1'}

No

{'C2': 'No', 'C1': 'Yes'}

KNeighborsClassifier

C2

Credit Risk Classification

The following classification assertions are based on the information provided on the case under consideration. The most probable or likely label judged by the classifier is C2 since its prediction probability is 60.0% compared to the 40.0% of C1. The influence of the features on the classifier's decision here can be ranked in the order F5, F2, F7, F3, F10, F8, F11, F4, F1, F9, F6. In fact, with the exception of F6, all the features are shown to have attributions, resulting in the predicted probabilities across the labels. The F5, F2, F7, and F1 have negative contributions, leading to the classifier's confidence in the validity of the C2 label and this is because they are the features that support labelling the case as C1. However, the positive features F3, F10, F8, F11, F4, and F9 tip the scales higher in favour of C2. Since the most influential features F5, F2, and F7 have negative contributions, it is not surprising that the classifier has the probability of C1 equal to just about 40.0%.

{'C1': '40.00%', 'C2': '60.00%'}

{'F5': 'fea_4', 'F2': 'fea_8', 'F7': 'fea_2', 'F3': 'fea_9', 'F10': 'fea_6', 'F8': 'fea_10', 'F11': 'fea_1', 'F4': 'fea_11', 'F1': 'fea_7', 'F9': 'fea_3', 'F6': 'fea_5'}

{'F4': 'F5', 'F8': 'F2', 'F2': 'F7', 'F9': 'F3', 'F6': 'F10', 'F10': 'F8', 'F1': 'F11', 'F11': 'F4', 'F7': 'F1', 'F3': 'F9', 'F5': 'F6'}

{'C2': 'C1', 'C1': 'C2'}

High

{'C1': 'Low', 'C2': 'High'}

SGDClassifier

C2

Airline Passenger Satisfaction

At a confidence level of 100.0%, the model labels this case as C2 and what this indicate is that there is no chance for C1 to be the correct label given the values of the input features. The above classification decision can be attributed to values for features such as F22, F5, F18, F20, F11, and F1. For this C2 prediction, the most important features are F22, F5, and F18. These are all positive features, meaning they strongly support the model's decision with respect to the case under consideration and a further push towards the assigned label is offered by the contributions of the other positive features such as F11, F1, F12, and F21. On the other hand, shifting the decision in the opposite direction are the negative features such as F20, F16, F10, F14, and F3. However, compared to F22, F5, and F18, the joint influence of the negative features mentioned above is weak. Finally, the values of the features F6 and F19, both with almost zero attributions, are not relevant when it comes to deciding the correct label for this case.

{'C1': '0.00%', 'C2': '100.00%'}

{'F22': 'Inflight wifi service', 'F5': 'Type of Travel', 'F18': 'Customer Type', 'F20': 'Online boarding', 'F11': 'On-board service', 'F1': 'Baggage handling', 'F12': 'Inflight service', 'F16': 'Departure\\/Arrival time convenient', 'F21': 'Leg room service', 'F10': 'Inflight entertainment', 'F8': 'Seat comfort', 'F14': 'Class', 'F3': 'Departure Delay in Minutes', 'F2': 'Cleanliness', 'F9': 'Gate location', 'F4': 'Gender', 'F15': 'Arrival Delay in Minutes', 'F7': 'Age', 'F13': 'Ease of Online booking', 'F17': 'Flight Distance', 'F6': 'Food and drink', 'F19': 'Checkin service'}

{'F7': 'F22', 'F4': 'F5', 'F2': 'F18', 'F12': 'F20', 'F15': 'F11', 'F17': 'F1', 'F19': 'F12', 'F8': 'F16', 'F16': 'F21', 'F14': 'F10', 'F13': 'F8', 'F5': 'F14', 'F21': 'F3', 'F20': 'F2', 'F10': 'F9', 'F1': 'F4', 'F22': 'F15', 'F3': 'F7', 'F9': 'F13', 'F6': 'F17', 'F11': 'F6', 'F18': 'F19'}

{'C2': 'C1', 'C1': 'C2'}

satisfied

{'C1': 'neutral or dissatisfied', 'C2': 'satisfied'}

SVC

C2

German Credit Evaluation

For the case under consideration here, there is a 70.83% probability that the true label is C2 and what this means is that there is also a 29.71% chance that C1 could be the correct label. Among the features, the top two most impactful are F6 and F1. The next features, ranked in order of the magnitude of their respective attribution are F5, F8, F3, F7, F4, F9, and F2. Out of the nine features, only three of them have values pushing for the prediction of label C1 while the rest are referred to as positive features given that their values motivate the prediction of class C2. The three attributes with the negative impact, shifting the prediction decision away from C2, are F1, F5, and F8. The collective influence of positive features is higher than that of negative features F1, F5, and F8.

{'C2': '70.83%', 'C1': '29.17%'}

{'F6': 'Checking account', 'F1': 'Duration', 'F5': 'Housing', 'F8': 'Saving accounts', 'F3': 'Sex', 'F7': 'Age', 'F4': 'Purpose', 'F9': 'Job', 'F2': 'Credit amount'}

{'F6': 'F6', 'F8': 'F1', 'F4': 'F5', 'F5': 'F8', 'F2': 'F3', 'F1': 'F7', 'F9': 'F4', 'F3': 'F9', 'F7': 'F2'}

{'C1': 'C2', 'C2': 'C1'}

Good Credit

{'C2': 'Good Credit', 'C1': 'Bad Credit'}

BernoulliNB

C1

German Credit Evaluation

The algorithm labels the data given as C1 and the prediction probabilities across the possible labels C1 and C2, respectively, are 51.39% and 48.61%. Judging based on the prediction probabilities, the algorithm shows signs of uncertainty in the above decision. F1, F7, F8, and F2 are the primary contributors to the classification verdict here. The contributions of F2, F9, and F3 are moderate, while those of F5, F4, and F7 are lower compared to the other variables. Positively supporting the classification are F1, F8, F4, and F6, while all the remaining variables have a negative impact that decreases the probability of C1 being the correct label. F7, F2, and F9 are negative variables that can be blamed for the uncertainty in the classification decision being made here.

{'C1': '51.39%', 'C2': '48.61%'}

{'F1': 'Housing', 'F7': 'Checking account', 'F8': 'Sex', 'F2': 'Purpose', 'F9': 'Job', 'F3': 'Duration', 'F5': 'Credit amount', 'F4': 'Age', 'F6': 'Saving accounts'}

{'F4': 'F1', 'F6': 'F7', 'F2': 'F8', 'F9': 'F2', 'F3': 'F9', 'F8': 'F3', 'F7': 'F5', 'F1': 'F4', 'F5': 'F6'}

{'C1': 'C1', 'C2': 'C2'}

Good Credit

{'C1': 'Good Credit', 'C2': 'Bad Credit'}

GradientBoostingClassifier

C2

Health Care Services Satisfaction Prediction

The label assignment decision is solely based on the values of the different input features passed to the classification algorithm since the values of these features are used as the basis to make the prediction judgments. The likelihood of any of the classes C2 and C1 being the correct label is 76.26% and 23.74%, respectively, therefore, it is valid to assert that the true label for this case is C2. From the attribution analysis, F10, F4, and F5 have the highest contribution to the decision, whilst F7 and F15 are the least relevant features. In between these two ends are the moderately influential features, such as F1, F16, F11, F12, and F13. Furthermore, the negative features F4, F11, F6, F2, F8, F14, and F7 can be blamed for the fact that the algorithm is not 100.0% certain about the labelling decision and this mainly because the negative features contribute towards choosing C1 instead of C2. Conversely, the positive features such as F10, F5, F1, F16, F12, F13, and F9 are the ones driving the decision higher towards C2.

{'C1': '23.74%', 'C2': '76.26%'}

{'F10': 'Exact diagnosis', 'F5': 'avaliablity of drugs', 'F4': 'lab services', 'F1': 'friendly health care workers', 'F16': 'Communication with dr', 'F11': 'Time waiting', 'F12': 'Specialists avaliable', 'F13': 'Modern equipment', 'F9': 'waiting rooms', 'F6': 'Check up appointment', 'F3': 'Hygiene and cleaning', 'F2': 'Admin procedures', 'F8': 'Time of appointment', 'F14': 'hospital rooms quality', 'F7': 'parking, playing rooms, caffes', 'F15': 'Quality\\/experience dr.'}

{'F9': 'F10', 'F13': 'F5', 'F12': 'F4', 'F11': 'F1', 'F8': 'F16', 'F2': 'F11', 'F7': 'F12', 'F10': 'F13', 'F14': 'F9', 'F1': 'F6', 'F4': 'F3', 'F3': 'F2', 'F5': 'F8', 'F15': 'F14', 'F16': 'F7', 'F6': 'F15'}

{'C1': 'C1', 'C2': 'C2'}

Satisfied

{'C1': 'Dissatisfied', 'C2': 'Satisfied'}

LogisticRegression

C1

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Here the classifier labels the given case as C1 with a moderately high confidence level. Specifically, the prediction likelihood of class C2 is only 21.67%. The main drivers for the classification above are F18, F37, F19, and F7. Among these top features, F18 and F37 have the most significant influence on the classification outcome, and they happen to have positive contributions, increasing the likelihood of class C1. On the other hand, the F7, F19, and F24 have a moderate negative contribution, reducing the odds of a C1 prediction. F31, F38, F22, and F4 are other notable positive features, while F29, F6, F35, and F10 are notable negative features. However, the classifier did not take into account all of the input features when arriving at the above-mentioned classification verdict; the features including F39, F17, and F13 are deemed irrelevant. To summarise, considering the attributions of influential features such as F18, F37, and F7, it is evident why the classifier is quite certain that C1 is the most probable label for the given case.

{'C2': '21.67%', 'C1': '78.33%'}

{'F18': 'X38', 'F37': 'X32', 'F7': 'X3', 'F19': 'X22', 'F24': 'X16', 'F31': 'X25', 'F38': 'X41', 'F22': 'X35', 'F29': 'X4', 'F6': 'X19', 'F35': 'X12', 'F10': 'X11', 'F28': 'X1', 'F34': 'X10', 'F4': 'X28', 'F21': 'X18', 'F5': 'X21', 'F9': 'X36', 'F20': 'X42', 'F3': 'X27', 'F39': 'X34', 'F17': 'X2', 'F13': 'X37', 'F30': 'X39', 'F41': 'X40', 'F1': 'X5', 'F36': 'X33', 'F25': 'X24', 'F14': 'X31', 'F8': 'X30', 'F33': 'X26', 'F15': 'X23', 'F26': 'X20', 'F27': 'X17', 'F32': 'X15', 'F11': 'X14', 'F40': 'X13', 'F23': 'X9', 'F16': 'X8', 'F12': 'X7', 'F2': 'X6', 'F42': 'X29'}

{'F35': 'F18', 'F29': 'F37', 'F2': 'F7', 'F20': 'F19', 'F14': 'F24', 'F23': 'F31', 'F39': 'F38', 'F32': 'F22', 'F3': 'F29', 'F17': 'F6', 'F10': 'F35', 'F9': 'F10', 'F40': 'F28', 'F8': 'F34', 'F26': 'F4', 'F16': 'F21', 'F19': 'F5', 'F33': 'F9', 'F38': 'F20', 'F25': 'F3', 'F31': 'F39', 'F1': 'F17', 'F34': 'F13', 'F36': 'F30', 'F37': 'F41', 'F41': 'F1', 'F30': 'F36', 'F22': 'F25', 'F28': 'F14', 'F27': 'F8', 'F24': 'F33', 'F21': 'F15', 'F18': 'F26', 'F15': 'F27', 'F13': 'F32', 'F12': 'F11', 'F11': 'F40', 'F7': 'F23', 'F6': 'F16', 'F5': 'F12', 'F4': 'F2', 'F42': 'F42'}

{'C2': 'C2', 'C1': 'C1'}

Yes

{'C2': 'No', 'C1': 'Yes'}

LogisticRegression

C3

Cab Surge Pricing System

The label assigned in this case by the classifier is C3, with a moderately high prediction confidence of 66.11%. Since the confidence level with respect to this C3 is not 100.0%, it is possible that one of the other labels is the true or correct label, and C1 is the next most likely label. The input variables F1, F5, F12, and F11 have a significant impact on the abovementioned prediction judgement. The value of features F1, F12, F9, and F8 contributes positively to the C3 label, instead of the other labels. F5, F11, F10, and F7 are the variables having a contradictory influence, shifting the final decision in the direction of the other labels. The remaining positive variables are F6, F2, F3, and F4. Of all the predictors, the ones that contributed the least to the prediction included F10, F3, F7, and F4. In summary, given the attributions of the predictors, it is clear why the classifier indicates that C3 is the correct class in this scenario.

{'C1': '31.78%', 'C3': '66.11%', 'C2': '2.11%'}

{'F1': 'Type_of_Cab', 'F5': 'Trip_Distance', 'F12': 'Destination_Type', 'F11': 'Cancellation_Last_1Month', 'F9': 'Confidence_Life_Style_Index', 'F8': 'Life_Style_Index', 'F6': 'Gender', 'F2': 'Var3', 'F10': 'Customer_Since_Months', 'F3': 'Var1', 'F7': 'Customer_Rating', 'F4': 'Var2'}

{'F2': 'F1', 'F1': 'F5', 'F6': 'F12', 'F8': 'F11', 'F5': 'F9', 'F4': 'F8', 'F12': 'F6', 'F11': 'F2', 'F3': 'F10', 'F9': 'F3', 'F7': 'F7', 'F10': 'F4'}

{'C2': 'C1', 'C3': 'C3', 'C1': 'C2'}

C2

{'C1': 'Low', 'C3': 'Medium', 'C2': 'High'}

SVM_poly

C4

Mobile Price-Range Classification

The classification assertions arrived here are mainly based on the influence and contributions of the different input variables. The prediction probabilities across the four possible classes C3, C1, C2, and C4 are 0.05%, 0.04%, 0.47%, and 99.45%, respectively. Therefore according to the classifier, the most likely class label for the case under investigation is C4 and it is quite sure that neither C2 nor C3 nor C1 is the true label here. The influence of F15 is shown to be the major contributing factor resulting in the prediction decision made by the classifier and the contributions of the remaining features such as F17, F2, F20, and F7 are moderately low compared to that of F15. The strong positive influence of F15 coupled with other positive features such as F20, F8, and F7 can explain the very high confidence level in the prediction decision. On the flip-side, the input features F17, F2, and F1 are considered negatives since their attributions marginally reduce the prediction probability of the C4 label.

{'C4': '99.45%', 'C2': '0.47%', 'C1': '0.04%', 'C3': '0.05%'}

{'F15': 'ram', 'F20': 'battery_power', 'F17': 'px_height', 'F2': 'px_width', 'F7': 'dual_sim', 'F1': 'four_g', 'F8': 'touch_screen', 'F18': 'int_memory', 'F4': 'pc', 'F9': 'n_cores', 'F11': 'fc', 'F5': 'clock_speed', 'F6': 'three_g', 'F16': 'sc_w', 'F14': 'wifi', 'F13': 'm_dep', 'F19': 'mobile_wt', 'F10': 'talk_time', 'F12': 'sc_h', 'F3': 'blue'}

{'F11': 'F15', 'F1': 'F20', 'F9': 'F17', 'F10': 'F2', 'F16': 'F7', 'F17': 'F1', 'F19': 'F8', 'F4': 'F18', 'F8': 'F4', 'F7': 'F9', 'F3': 'F11', 'F2': 'F5', 'F18': 'F6', 'F13': 'F16', 'F20': 'F14', 'F5': 'F13', 'F6': 'F19', 'F14': 'F10', 'F12': 'F12', 'F15': 'F3'}

{'C1': 'C4', 'C4': 'C2', 'C2': 'C1', 'C3': 'C3'}

r1

{'C4': 'r1', 'C2': 'r2', 'C1': 'r3', 'C3': 'r4'}

BernoulliNB

C2

Job Change of Data Scientists

The prediction likelihood of class C2 is 84.87%, making it the most probable label for the given case. When making the above prediction, the most relevant features considered are F2, F9, F5, and F7. Conversely, F3, F8, and F4 are the least influential features, with their values receiving little consideration from the model regarding this classification. Assessing the direction of influence or contribution of the features suggest that there is a split between the number of features with a negative influence and those with a positive influence. However, only two of the negative features, F9 and F6, have a somewhat high influence; the others , F1, F3, F8, and F12, have a lower negative influence. To put it concisely, the combined influence of the positive features, such as F2, F7, F11, F10, and F5, outweighs that of all the negative features combined, therefore, it is entirely plausible to see such confidence level of the model for the classification here.

{'C1': '15.13%', 'C2': '84.87%'}

{'F2': 'city', 'F7': 'enrolled_university', 'F9': 'relevent_experience', 'F5': 'city_development_index', 'F6': 'experience', 'F10': 'education_level', 'F11': 'major_discipline', 'F4': 'last_new_job', 'F1': 'gender', 'F3': 'company_size', 'F8': 'company_type', 'F12': 'training_hours'}

{'F3': 'F2', 'F6': 'F7', 'F5': 'F9', 'F1': 'F5', 'F9': 'F6', 'F7': 'F10', 'F8': 'F11', 'F12': 'F4', 'F4': 'F1', 'F10': 'F3', 'F11': 'F8', 'F2': 'F12'}

{'C1': 'C1', 'C2': 'C2'}

Leave

{'C1': 'Stay', 'C2': 'Leave'}

MLPClassifier

C1

Annual Income Earnings

With respect to the given case, the most probable label for the given case is C1, with a 99.81% chance of being the correct label, therefore the probability of C2 is only 0.19% for this case. Among the input variables, only four features are shown to have a negative influence on the classification decision above: F11, F2, F5, and F6 since their contributions to the decision only favour labelling the given case as C2 instead. On the flip side, pushing the classification strongly towards C1 are the features F3, F1, F7, and F10 explaining the very high confidence in the choice of label assigned here.

{'C1': '99.81%', 'C2': '0.19%'}

{'F3': 'Capital Gain', 'F1': 'Marital Status', 'F7': 'Capital Loss', 'F10': 'Age', 'F8': 'Hours per week', 'F4': 'Education', 'F11': 'Occupation', 'F2': 'Country', 'F13': 'Relationship', 'F12': 'Workclass', 'F5': 'Sex', 'F14': 'fnlwgt', 'F6': 'Education-Num', 'F9': 'Race'}

{'F11': 'F3', 'F6': 'F1', 'F12': 'F7', 'F1': 'F10', 'F13': 'F8', 'F4': 'F4', 'F7': 'F11', 'F14': 'F2', 'F8': 'F13', 'F2': 'F12', 'F10': 'F5', 'F3': 'F14', 'F5': 'F6', 'F9': 'F9'}

{'C2': 'C1', 'C1': 'C2'}

Under 50K

{'C1': 'Under 50K', 'C2': 'Above 50K'}

GaussianNB

C1

Tic-Tac-Toe Strategy

For a particular case, the model predicted the class designation C1 with 75.50% confidence. Based on the attributions analysis, the feature that had the biggest impact on the final labelling decision were the F4 and F2, which happened to strongly support the assignment of label C1. Contributing differently to F4, the feature F5 is the top negative feature, reducing the odds that C1 is the correct label. F8, F9, F5, and F1 have similar influences on the model in terms of the magnitude of their contributions or attributions, however, the directions of their respective effects are different: the features F8 and F9 positively support the model, driving the prediction towards class C1, while F5 and F1 work against it. F6, F3, and F7 are features that have little effect on the model when assigning the label for the given case, and all of them negatively contributed to the C1 class selection. Among all the features with little contribution to the prediction verdict above, F7 is the least relevant.

{'C2': '24.50%', 'C1': '75.50%'}

{'F4': 'middle-middle-square', 'F2': ' top-right-square', 'F8': 'bottom-middle-square', 'F9': 'middle-right-square', 'F5': 'bottom-left-square', 'F1': 'bottom-right-square', 'F6': 'top-left-square', 'F3': 'middle-left-square', 'F7': 'top-middle-square'}

{'F5': 'F4', 'F3': 'F2', 'F8': 'F8', 'F6': 'F9', 'F7': 'F5', 'F9': 'F1', 'F1': 'F6', 'F4': 'F3', 'F2': 'F7'}

{'C2': 'C2', 'C1': 'C1'}

player B win

{'C2': 'player B lose', 'C1': 'player B win'}

RandomForestClassifier

C4

Mobile Price-Range Classification

The model reveals that C3 and C2 each has a zero prediction probability, while C1 has a 3.85%. This indicates that C4 is the most likely label for the present context with approximately 96.15% certainty. F16, F15, and F10 are the most important elements driving the above classification, whereas F2, F14, F9, F8, and F12 are the least important. The intermediate elements, which comprise F11, F20, and F5, have varied degrees of influence, ranging from moderate to low. F11 is the only with a negative contribution among the top influential features, F16, F15, F10, F11, and F20, skewing the forecast slightly towards a different possible label. Furthermore, the top two positive elements, F15 and F16, have a greater effect than the sum of all the negative ones.

{'C3': '0.00%', 'C2': '0.00%', 'C1': '3.85%', 'C4': '96.15%'}

{'F15': 'ram', 'F16': 'battery_power', 'F10': 'px_width', 'F11': 'int_memory', 'F20': 'pc', 'F5': 'touch_screen', 'F13': 'four_g', 'F19': 'm_dep', 'F4': 'px_height', 'F3': 'clock_speed', 'F18': 'sc_h', 'F6': 'n_cores', 'F17': 'talk_time', 'F1': 'blue', 'F7': 'dual_sim', 'F2': 'fc', 'F14': 'mobile_wt', 'F9': 'sc_w', 'F8': 'wifi', 'F12': 'three_g'}

{'F11': 'F15', 'F1': 'F16', 'F10': 'F10', 'F4': 'F11', 'F8': 'F20', 'F19': 'F5', 'F17': 'F13', 'F5': 'F19', 'F9': 'F4', 'F2': 'F3', 'F12': 'F18', 'F7': 'F6', 'F14': 'F17', 'F15': 'F1', 'F16': 'F7', 'F3': 'F2', 'F6': 'F14', 'F13': 'F9', 'F20': 'F8', 'F18': 'F12'}

{'C4': 'C3', 'C3': 'C2', 'C1': 'C1', 'C2': 'C4'}

r4

{'C3': 'r1', 'C2': 'r2', 'C1': 'r3', 'C4': 'r4'}

LogisticRegression

C1

House Price Classification

The prediction is that class label C1 is very likely the correct label, given that the associated confidence level is 99.93%. The features F9, F10, and F7 appear to have very smaller or little impact on the prediction of C1 compared to F3, F11, F1, F12, and F6, according to the attribution analysis. F3 and F11 are the features with the highest impact on the model's output prediction verdict above and fortunately the values of these features positively support the C1 classification verdict. Other positive features increasing the odds in favour of C1 include F1, F12, F2, and F4. On the contrarily, the feature F6 negatively influences the model's prediction of C1, shifting the verdict in the opposite direction. It is important to note that, only the features F6, F13, and F7 have negative attributions, while all the remaining ones have positive attributions. The joint positive attribution outweighs the negative attributions from F6, F13, and F7.

{'C2': '0.07%', 'C1': '99.93%'}

{'F3': 'LSTAT', 'F11': 'RM', 'F1': 'PTRATIO', 'F12': 'RAD', 'F6': 'CHAS', 'F2': 'TAX', 'F4': 'CRIM', 'F8': 'DIS', 'F13': 'AGE', 'F5': 'B', 'F7': 'ZN', 'F10': 'NOX', 'F9': 'INDUS'}

{'F13': 'F3', 'F6': 'F11', 'F11': 'F1', 'F9': 'F12', 'F4': 'F6', 'F10': 'F2', 'F1': 'F4', 'F8': 'F8', 'F7': 'F13', 'F12': 'F5', 'F2': 'F7', 'F5': 'F10', 'F3': 'F9'}

{'C1': 'C2', 'C2': 'C1'}

High

{'C2': 'Low', 'C1': 'High'}

GaussianNB

C2

Tic-Tac-Toe Strategy

For the given case, the model predicted the class label C2 with a certainty of around 75.50%. By far, the feature with the most impact on the final classification was F8, which positively supports the decision. Feature F7 was the feature that contributed the most to pushing away the classification decision from C2, that is, they are decreasing the likelihood of C2 being the correct label. F6, F4, F7, and F1 all had a similar impact on the classification. However, the direction of influence is different, with features F6 and F4 pushing the model's decision to class C2 and features F7 and F1 doing the opposite. F5, F3, and F9 are the features that had closer to negligible impact on the final classification, all of which had a negative contribution towards class C2.

{'C1': '24.50%', 'C2': '75.50%'}

{'F8': 'middle-middle-square', 'F2': ' top-right-square', 'F6': 'bottom-middle-square', 'F4': 'middle-right-square', 'F7': 'bottom-left-square', 'F1': 'bottom-right-square', 'F5': 'top-left-square', 'F3': 'middle-left-square', 'F9': 'top-middle-square'}

{'F5': 'F8', 'F3': 'F2', 'F8': 'F6', 'F6': 'F4', 'F7': 'F7', 'F9': 'F1', 'F1': 'F5', 'F4': 'F3', 'F2': 'F9'}

{'C1': 'C1', 'C2': 'C2'}

player B win

{'C1': 'player B lose', 'C2': 'player B win'}

LogisticRegression

C2

Food Ordering Customer Churn Prediction

Judging based on the values of the input features, a decision is made by the classifier to label the given data as C2 with a prediction confidence equal to 84.90%. The major influential features resulting in the classification here are F24, F8, F27, and F6. F24 and F8 are identified as the most negative features, with contributions that lead to a decrease in the classification confidence of label C2. F6 and F27, on the other hand, are the top positive features, leading the classifier to label the case as C2. Other notable negative features are F20, F32, and F3 while other notable positives are F25, F42, F37, and F17. Unlike all those mentioned above, F10, F44, F43, and F7 are among the many irrelevant features with negligible contributions to the classification decision here.

{'C2': '84.90%', 'C1': '15.10%'}

{'F24': 'Unaffordable', 'F8': 'Late Delivery', 'F6': 'Good Food quality', 'F27': 'Perference(P2)', 'F20': 'Delay of delivery person picking up food', 'F32': 'Influence of rating', 'F3': 'Wrong order delivered', 'F25': 'Time saving', 'F42': 'Ease and convenient', 'F37': 'Order Time', 'F17': 'Google Maps Accuracy', 'F11': 'Freshness ', 'F41': 'Politeness', 'F23': 'Long delivery time', 'F13': 'Good Road Condition', 'F31': 'High Quality of package', 'F29': 'Monthly Income', 'F9': 'Missing item', 'F21': 'More Offers and Discount', 'F26': 'Unavailability', 'F10': 'Influence of time', 'F44': 'Delivery person ability', 'F7': 'Low quantity low time', 'F43': 'Less Delivery time', 'F16': 'Residence in busy location', 'F5': 'Maximum wait time', 'F34': 'Temperature', 'F18': 'Good Taste ', 'F4': 'Number of calls', 'F19': 'Age', 'F35': 'Order placed by mistake', 'F1': 'Delay of delivery person getting assigned', 'F45': 'Family size', 'F30': 'Bad past experience', 'F2': 'Poor Hygiene', 'F28': 'Health Concern', 'F38': 'Self Cooking', 'F33': 'Good Tracking system', 'F46': 'Easy Payment option', 'F14': 'More restaurant choices', 'F39': 'Perference(P1)', 'F40': 'Educational Qualifications', 'F12': 'Occupation', 'F15': 'Marital Status', 'F36': 'Gender', 'F22': 'Good Quantity'}

{'F23': 'F24', 'F19': 'F8', 'F15': 'F6', 'F9': 'F27', 'F26': 'F20', 'F38': 'F32', 'F27': 'F3', 'F11': 'F25', 'F10': 'F42', 'F31': 'F37', 'F34': 'F17', 'F43': 'F11', 'F42': 'F41', 'F24': 'F23', 'F35': 'F13', 'F40': 'F31', 'F5': 'F29', 'F28': 'F9', 'F14': 'F21', 'F22': 'F26', 'F30': 'F10', 'F37': 'F44', 'F36': 'F7', 'F39': 'F43', 'F33': 'F16', 'F32': 'F5', 'F44': 'F34', 'F45': 'F18', 'F41': 'F4', 'F1': 'F19', 'F29': 'F35', 'F25': 'F1', 'F7': 'F45', 'F21': 'F30', 'F20': 'F2', 'F18': 'F28', 'F17': 'F38', 'F16': 'F33', 'F13': 'F46', 'F12': 'F14', 'F8': 'F39', 'F6': 'F40', 'F4': 'F12', 'F3': 'F15', 'F2': 'F36', 'F46': 'F22'}

{'C2': 'C2', 'C1': 'C1'}

Return

{'C2': 'Return', 'C1': 'Go Away'}

LogisticRegression

C1

Used Cars Price-Range Prediction

The prediction model predicts C1 for the case under consideration since the likelihood of C2 which is equal to 30.05%, is lower than that of C1 and this verdict came about mainly based on the values of the input features passed to the model. F6, F7, and F9 are identified as the most influential features with higher impact on the model's labelling decision here and among them F6 and F7 have negative contributions decreasing the model's response towards the assigned label. Furthermore, F9, F4, and F1 have a positive impact on the model and in effect pushes the decision higher towards C1, while F8, F10, and F5 have identical direction of impact as that of F7 and F6. Finally, F3 is the least relevant feature, therefore, its negative attribution has little effect on the model in this case and also the positive influence of F2 further supports the assigned label.

{'C2': '30.05%', 'C1': '69.95%'}

{'F6': 'Fuel_Type', 'F7': 'Seats', 'F9': 'car_age', 'F10': 'Name', 'F5': 'Owner_Type', 'F4': 'Power', 'F1': 'Engine', 'F8': 'Transmission', 'F2': 'Mileage', 'F3': 'Kilometers_Driven'}

{'F7': 'F6', 'F10': 'F7', 'F5': 'F9', 'F6': 'F10', 'F9': 'F5', 'F4': 'F4', 'F3': 'F1', 'F8': 'F8', 'F2': 'F2', 'F1': 'F3'}

{'C1': 'C2', 'C2': 'C1'}

High

{'C2': 'Low', 'C1': 'High'}

DecisionTreeClassifier

C2

Credit Risk Classification

The model assigned the label C2 to the given instance since its associated likelihood is far higher than C1. The most relevant features controlling the prediction decision above are F10, F3, and F11. The less relevant ones include F1, F6, and F5. The majority of the features have values, swinging the verdict towards the other class, C1. The only features increasing the likelihood or probability of C2 being the correct label are F10, F9, and F6. Given that only few features positively contribute to arriving at the C2 prediction, it is very strange that the model has 100.0% confidence in its prediction for the selected instance.

{'C1': '0.00%', 'C2': '100.00%'}

{'F10': 'fea_4', 'F3': 'fea_8', 'F11': 'fea_5', 'F7': 'fea_2', 'F2': 'fea_1', 'F8': 'fea_9', 'F4': 'fea_11', 'F9': 'fea_6', 'F1': 'fea_10', 'F6': 'fea_7', 'F5': 'fea_3'}

{'F4': 'F10', 'F8': 'F3', 'F5': 'F11', 'F2': 'F7', 'F1': 'F2', 'F9': 'F8', 'F11': 'F4', 'F6': 'F9', 'F10': 'F1', 'F7': 'F6', 'F3': 'F5'}

{'C1': 'C1', 'C2': 'C2'}

High

{'C1': 'Low', 'C2': 'High'}

RandomForestClassifier

C1

Music Concert Attendance

There is an 80.0% chance that the true label for the given case is C1. Nine out of twenty features have a positive impact. Most features have a moderately low positive or negative impact, with the exception of F20, F16, and F14 and it appears as if F20 has an extremely negative impact, while F16 and F14 have the greater positive impacts. F18 has positive impacts, whereas the attributions of the features F8 and F2 are negatives. The least important features include F4, F1, F17, F5, F7, F6, and F10 with varying smaller effects.

{'C1': '80.00%', 'C2': '20.00%'}

{'F20': 'X11', 'F16': 'X1', 'F14': 'X6', 'F8': 'X10', 'F18': 'X14', 'F2': 'X16', 'F9': 'X13', 'F12': 'X12', 'F15': 'X3', 'F4': 'X2', 'F1': 'X15', 'F17': 'X4', 'F5': 'X7', 'F7': 'X17', 'F6': 'X8', 'F10': 'X5', 'F11': 'X18', 'F19': 'X19', 'F3': 'X9', 'F13': 'X20'}

{'F11': 'F20', 'F1': 'F16', 'F6': 'F14', 'F10': 'F8', 'F14': 'F18', 'F16': 'F2', 'F13': 'F9', 'F12': 'F12', 'F3': 'F15', 'F2': 'F4', 'F15': 'F1', 'F4': 'F17', 'F7': 'F5', 'F17': 'F7', 'F8': 'F6', 'F5': 'F10', 'F18': 'F11', 'F19': 'F19', 'F9': 'F3', 'F20': 'F13'}

{'C1': 'C1', 'C2': 'C2'}

< 10k

{'C1': '< 10k', 'C2': '> 10k'}

RandomForestClassifier

C1

Employee Attrition

There is disagreement about which label is acceptable for the case under consideration since the model is unsure which of the two labels is right. The confusion in the aforementioned classification may be attributable only to the effect of F9. F9 is by far the most influential variable, with a negative contribution that reduces the chance of label C1 being the correct label in the given case substantially; supporting the that case should be labelled as C2. Compared to the influence of F9, the remaining variables have a moderate to low effect on the classification decision made here for the case under consideration. F18, F2, and F13 are notable moderately key variables, with positive contributions boosting the likelihood of label C1. F6, F27, F24, F29, F26, F22, F14, F23, F28, and F1 are not among the features demonstrated to contribute to the classification above; since they have very insignificant impact on the model's conclusion here.

{'C1': '50.00%', 'C2': '50.00%'}

{'F9': 'OverTime', 'F2': 'MaritalStatus', 'F18': 'EnvironmentSatisfaction', 'F13': 'JobSatisfaction', 'F19': 'JobRole', 'F25': 'WorkLifeBalance', 'F3': 'Education', 'F16': 'Gender', 'F30': 'BusinessTravel', 'F5': 'StockOptionLevel', 'F20': 'YearsInCurrentRole', 'F15': 'RelationshipSatisfaction', 'F8': 'YearsWithCurrManager', 'F17': 'YearsSinceLastPromotion', 'F11': 'PercentSalaryHike', 'F10': 'JobInvolvement', 'F7': 'DistanceFromHome', 'F12': 'EducationField', 'F21': 'YearsAtCompany', 'F4': 'MonthlyRate', 'F27': 'PerformanceRating', 'F24': 'Department', 'F6': 'TotalWorkingYears', 'F29': 'NumCompaniesWorked', 'F26': 'MonthlyIncome', 'F22': 'JobLevel', 'F14': 'HourlyRate', 'F23': 'TrainingTimesLastYear', 'F28': 'DailyRate', 'F1': 'Age'}

{'F26': 'F9', 'F25': 'F2', 'F28': 'F18', 'F30': 'F13', 'F24': 'F19', 'F20': 'F25', 'F27': 'F3', 'F23': 'F16', 'F17': 'F30', 'F10': 'F5', 'F14': 'F20', 'F18': 'F15', 'F16': 'F8', 'F15': 'F17', 'F9': 'F11', 'F29': 'F10', 'F3': 'F7', 'F22': 'F12', 'F13': 'F21', 'F7': 'F4', 'F19': 'F27', 'F21': 'F24', 'F11': 'F6', 'F8': 'F29', 'F6': 'F26', 'F5': 'F22', 'F4': 'F14', 'F12': 'F23', 'F2': 'F28', 'F1': 'F1'}

{'C2': 'C1', 'C1': 'C2'}

Stay

{'C1': 'Leave', 'C2': 'Leave'}

KNeighborsClassifier

C1

German Credit Evaluation

For the case under consideration, the probability of C2 being the correct label is only 12.50%, implying that there is an 87.50% chance that C1 is the true label. The decision above was arrived at mainly based on the values of the following variables F3, F6, and F2. Among these top variables, only F3 has a very strong positive impact on the model, increasing the likelihood of C1 prediction. The most important variables decreasing the prediction are F6 and F2 and the remaining two shifting the verdict away from C1 are F8 and F1. F4 and F9 are the lowest-ranked variables, less important to the prediction made here since they have a moderately low positive impact on the model.

{'C1': '87.50%', 'C2': '12.50%'}

{'F3': 'Checking account', 'F6': 'Saving accounts', 'F2': 'Purpose', 'F8': 'Sex', 'F1': 'Duration', 'F7': 'Housing', 'F5': 'Age', 'F4': 'Job', 'F9': 'Credit amount'}

{'F6': 'F3', 'F5': 'F6', 'F9': 'F2', 'F2': 'F8', 'F8': 'F1', 'F4': 'F7', 'F1': 'F5', 'F3': 'F4', 'F7': 'F9'}

{'C1': 'C1', 'C2': 'C2'}

Good Credit

{'C1': 'Good Credit', 'C2': 'Bad Credit'}

DecisionTreeClassifier

C2

Hotel Satisfaction

With a high degree of confidence, close to 100 percent, the classifier's final label choice for the given case is C2 due to the predicted probability distribution between the class labels. Analysis of the attributions of the input features indicates that the most relevant features driving the classification above are F10, F7, F11, and F4, whereas F14 and F5 are shown to have little contribution to the decision. Furthermore, only four of the features have a negative influence, swinging the classifier decision in this case towards the C1 label and they are F7, F2, F14, and F5. However, except for F7, the contribution of the other negative features is very low when compared with the top positive features such as F11, F6, and F4.

{'C1': '0.00%', 'C2': '100.00%'}

{'F10': 'Type of Travel', 'F7': 'Hotel wifi service', 'F11': 'Other service', 'F4': 'Type Of Booking', 'F6': 'Checkin\\/Checkout service', 'F1': 'Age', 'F8': 'purpose_of_travel', 'F15': 'Common Room entertainment', 'F12': 'Food and drink', 'F2': 'Stay comfort', 'F3': 'Hotel location', 'F9': 'Departure\\/Arrival convenience', 'F13': 'Gender', 'F14': 'Ease of Online booking', 'F5': 'Cleanliness'}

{'F3': 'F10', 'F6': 'F7', 'F14': 'F11', 'F4': 'F4', 'F13': 'F6', 'F5': 'F1', 'F2': 'F8', 'F12': 'F15', 'F10': 'F12', 'F11': 'F2', 'F9': 'F3', 'F7': 'F9', 'F1': 'F13', 'F8': 'F14', 'F15': 'F5'}

{'C2': 'C1', 'C1': 'C2'}

satisfied

{'C1': 'dissatisfied', 'C2': 'satisfied'}

KNNClassifier

C1

Car Acceptability Valuation

Based on the values of the six input features, the model assigned the label C1 to the given case with a higher degree of confidence and according to the model used here, there is a near-zero chance that the label could be C2. Influencing the prediction assessment above are the top four features, F6, F4, and F2, whereas, the least significant feature here is F1. Among the input features, only two, F4 and F1, contradict the label assignment decision above since their values are shifting the label decision in the C2 direction. However, the joint attribution of these features is outweighed by the remaining four features, F6, F2, F5, and F3. This could explain why the model is very certain about the C1 prediction made for the case under consideration.

{'C2': '0.00%', 'C1': '100.00%'}

{'F6': 'persons', 'F4': 'buying', 'F2': 'lug_boot', 'F5': 'maint', 'F3': 'safety', 'F1': 'doors'}

{'F4': 'F6', 'F1': 'F4', 'F5': 'F2', 'F2': 'F5', 'F6': 'F3', 'F3': 'F1'}

{'C1': 'C2', 'C2': 'C1'}

Acceptable

{'C2': 'Unacceptable', 'C1': 'Acceptable'}

RandomForestClassifier

C2

Printer Sales

The most likely label for the given data is C2 and this decision is as the result of the variables passed to the classifier. F2, F1, F20, and F19 are the primary contributors to the aforementioned prediction output. F18, F14, F22, F10, F16, and F4, on the other hand, make insignificant contributions to the classifier labelling the given example. F13 and F3, as well as F5, F23, have a moderate influence on the label selection. The classifier's confidence in the label decision above might be explained away by comparing the greater positive attributions of F3, F13, F2, and F19 to the negative attributions of F5, F6, F20, F12, F1, and F8.

{'C1': '20.00%', 'C2': '80.00%'}

{'F19': 'X24', 'F2': 'X1', 'F20': 'X8', 'F1': 'X21', 'F5': 'X4', 'F3': 'X10', 'F13': 'X3', 'F23': 'X15', 'F7': 'X9', 'F15': 'X23', 'F21': 'X25', 'F9': 'X7', 'F12': 'X22', 'F17': 'X11', 'F6': 'X17', 'F24': 'X18', 'F25': 'X26', 'F8': 'X13', 'F26': 'X6', 'F11': 'X20', 'F10': 'X16', 'F4': 'X19', 'F22': 'X2', 'F18': 'X12', 'F16': 'X5', 'F14': 'X14'}

{'F24': 'F19', 'F1': 'F2', 'F8': 'F20', 'F21': 'F1', 'F4': 'F5', 'F10': 'F3', 'F3': 'F13', 'F15': 'F23', 'F9': 'F7', 'F23': 'F15', 'F25': 'F21', 'F7': 'F9', 'F22': 'F12', 'F11': 'F17', 'F17': 'F6', 'F18': 'F24', 'F26': 'F25', 'F13': 'F8', 'F6': 'F26', 'F20': 'F11', 'F16': 'F10', 'F19': 'F4', 'F2': 'F22', 'F12': 'F18', 'F5': 'F16', 'F14': 'F14'}

{'C2': 'C1', 'C1': 'C2'}

More

{'C1': 'Less', 'C2': 'More'}

LogisticRegression

C1

Flight Price-Range Classification

Because the prediction algorithm outputs reveal that the likelihood of C1 being the correct label is equal to 93.02%; hence, there is only a little possibility that the true label for the provided data instance is either of the other labels, C2, C3, and C4. The variables F7, F12, F8, and F3 are the most crucial ones driving the label assignment conclusion above, whereas F5, F2, and F10 are the least vital ones. Taking into account the direction of effect of each input feature, as demonstrated by the attribution analysis, it is possible to deduce that the positive features driving the prediction upward towards C1 are F7, F4, F8, F11, F3, F12, and F2. The negative contributions of F9, F6, F10, F5, and F1 are ascribed to the marginal uncertainty in the expected output decision. When the predicted probabilities across the classes are considered, it is possible to infer that the combined positive contribution outranks the negative contributions; therefore, the algorithm is certain that C1 is the real label.

{'C1': '93.02%', 'C3': '6.97%', 'C4': '0.01%', 'C2': '0.0%'}

{'F7': 'Total_Stops', 'F12': 'Airline', 'F3': 'Destination', 'F8': 'Journey_day', 'F9': 'Source', 'F6': 'Dep_hour', 'F11': 'Duration_hours', 'F1': 'Dep_minute', 'F4': 'Duration_mins', 'F5': 'Arrival_minute', 'F2': 'Arrival_hour', 'F10': 'Journey_month'}

{'F12': 'F7', 'F9': 'F12', 'F11': 'F3', 'F1': 'F8', 'F10': 'F9', 'F3': 'F6', 'F7': 'F11', 'F4': 'F1', 'F8': 'F4', 'F6': 'F5', 'F5': 'F2', 'F2': 'F10'}

{'C1': 'C1', 'C2': 'C3', 'C4': 'C4', 'C3': 'C2'}

Low

{'C1': 'Low', 'C3': 'Moderate', 'C4': 'High', 'C2': 'Special'}

LogisticRegression

C1

Food Ordering Customer Churn Prediction

Based mainly on the values of the input variables F29, F9, F2, and F32, the predictor classifies the case as C1 with a 90.15% labelling confidence level, indicating that there is only a 9.85% probability that the right label could be C2. Variables that contribute positively to the prediction verdict include F29, F1, F8, and F32. The values of these variables increase the odds of the model labelling the given case as C1. On the other hand, F2, F9, F18, and F42 are the variables influencing the prediction decision in favour of C2 instead of C1. Simply put, the values of these negative variables contradict the label assigned here and finally, the model places little emphasis on the values of features such as F25, F3, F7, and F12 when determining the correct label in this instance, as they have nearly zero influence.

{'C2': '9.85%', 'C1': '90.15%'}

{'F29': 'Unaffordable', 'F2': 'Perference(P2)', 'F9': 'Influence of rating', 'F32': 'Good Food quality', 'F8': 'Delay of delivery person picking up food', 'F1': 'Less Delivery time', 'F18': 'Freshness ', 'F6': 'Politeness', 'F42': 'Ease and convenient', 'F45': 'More restaurant choices', 'F30': 'Missing item', 'F4': 'Order Time', 'F24': 'Gender', 'F14': 'Time saving', 'F13': 'Unavailability', 'F27': 'Late Delivery', 'F43': 'Temperature', 'F11': 'High Quality of package', 'F22': 'Long delivery time', 'F33': 'Poor Hygiene', 'F3': 'Low quantity low time', 'F25': 'Delivery person ability', 'F7': 'Number of calls', 'F12': 'Google Maps Accuracy', 'F38': 'Residence in busy location', 'F35': 'Good Taste ', 'F31': 'Maximum wait time', 'F41': 'Influence of time', 'F16': 'Good Road Condition', 'F28': 'Age', 'F21': 'Order placed by mistake', 'F44': 'Wrong order delivered', 'F23': 'Delay of delivery person getting assigned', 'F10': 'Family size', 'F39': 'Bad past experience', 'F17': 'Health Concern', 'F20': 'Self Cooking', 'F37': 'Good Tracking system', 'F34': 'More Offers and Discount', 'F36': 'Easy Payment option', 'F26': 'Perference(P1)', 'F46': 'Educational Qualifications', 'F15': 'Monthly Income', 'F40': 'Occupation', 'F19': 'Marital Status', 'F5': 'Good Quantity'}

{'F23': 'F29', 'F9': 'F2', 'F38': 'F9', 'F15': 'F32', 'F26': 'F8', 'F39': 'F1', 'F43': 'F18', 'F42': 'F6', 'F10': 'F42', 'F12': 'F45', 'F28': 'F30', 'F31': 'F4', 'F2': 'F24', 'F11': 'F14', 'F22': 'F13', 'F19': 'F27', 'F44': 'F43', 'F40': 'F11', 'F24': 'F22', 'F20': 'F33', 'F36': 'F3', 'F37': 'F25', 'F41': 'F7', 'F34': 'F12', 'F33': 'F38', 'F45': 'F35', 'F32': 'F31', 'F30': 'F41', 'F35': 'F16', 'F1': 'F28', 'F29': 'F21', 'F27': 'F44', 'F25': 'F23', 'F7': 'F10', 'F21': 'F39', 'F18': 'F17', 'F17': 'F20', 'F16': 'F37', 'F14': 'F34', 'F13': 'F36', 'F8': 'F26', 'F6': 'F46', 'F5': 'F15', 'F4': 'F40', 'F3': 'F19', 'F46': 'F5'}

{'C2': 'C2', 'C1': 'C1'}

Go Away

{'C2': 'Return', 'C1': 'Go Away'}

LogisticRegression

C2

Air Quality Prediction

The classification output decision is based solely on the information supplied to the model and it predicts class C2 with a higher confidence level, equal to 94.10%, indicating the model is very confident that the correct label for the given case is not either class C3 or class C4 or class C1. The classification output decision with regards to the given case boils down to the values of the features F5, F2, F6, and F4, which are shown to have the most significant influence on the model. Among these relevant features, only F5, F4, and F2 have a positive impact, increasing the response towards labelling the case as C2. Conversely, the remaining ones, F6 and F3, have negative attributions, decreasing the odds of the assigned label. Finally, feature F1 has little impact on this prediction among the features since its value received little consideration from the model.

{'C3': '0.00%', 'C4': '0.53%', 'C2': '94.10%', 'C1': '5.37%'}

{'F5': 'MQ5', 'F2': 'MQ6', 'F4': 'MQ3', 'F6': 'MQ4', 'F3': 'MQ1', 'F1': 'MQ2'}

{'F5': 'F5', 'F6': 'F2', 'F3': 'F4', 'F4': 'F6', 'F1': 'F3', 'F2': 'F1'}

{'C3': 'C3', 'C1': 'C4', 'C4': 'C2', 'C2': 'C1'}

Cleaning

{'C3': 'Preparing meals', 'C4': 'Presence of smoke', 'C2': 'Cleaning', 'C1': 'Other'}

SVC

C1

German Credit Evaluation

The model predicts that this case is likely C1 with a confidence level equal to 66.80%, meaning there is a 33.20% chance that it could be C2 instead. According to the analysis for this case under consideration, the most relevant features considered by the model are F6, F1, F3, F8, and F5, however, the least relevant features are F2 and F9. The F3, F8, F5, and F7 can be regarded as positively supporting features given that they increase the model's response in favour of the prediction conclusion above. In contrast, the F6, F1, and F4 are the features supporting the prediction of the alternative or other class label C2. Even though only a small number of features support the prediction of C2, their collective or joint influence is enough to upset the joint influence of the other features, leading to the uncertainty of the C1 prediction.

{'C1': '66.80%', 'C2': '33.20%'}

{'F6': 'Saving accounts', 'F1': 'Duration', 'F3': 'Checking account', 'F8': 'Sex', 'F5': 'Age', 'F7': 'Purpose', 'F4': 'Housing', 'F2': 'Job', 'F9': 'Credit amount'}

{'F5': 'F6', 'F8': 'F1', 'F6': 'F3', 'F2': 'F8', 'F1': 'F5', 'F9': 'F7', 'F4': 'F4', 'F3': 'F2', 'F7': 'F9'}

{'C2': 'C1', 'C1': 'C2'}

Good Credit

{'C1': 'Good Credit', 'C2': 'Bad Credit'}

SGDClassifier

C1

House Price Classification

Based on the values of the input variables resulting in the predicted likelihoods across the classes, the classification algorithm is confident that the right label for the provided data is C1. According to the algorithm, there is no possibility that C2 is the correct label. However, the attributions of F5, F8, F11, and F9 indicate that the correct label might be C2 rather than C1. The top four variables are F3, F2, F7, and F1, all of which have a positive influence on the algorithm's prediction output, hence confirming the C1 classification. This conclusion is further supported by the contributions of F10, F4, F13, F6, and F12, which are also positive variables.

{'C2': '0.0%', 'C1': '100.0%'}

{'F3': 'AGE', 'F2': 'RAD', 'F7': 'LSTAT', 'F1': 'RM', 'F5': 'DIS', 'F8': 'CHAS', 'F10': 'ZN', 'F4': 'CRIM', 'F11': 'TAX', 'F12': 'B', 'F6': 'PTRATIO', 'F13': 'INDUS', 'F9': 'NOX'}

{'F7': 'F3', 'F9': 'F2', 'F13': 'F7', 'F6': 'F1', 'F8': 'F5', 'F4': 'F8', 'F2': 'F10', 'F1': 'F4', 'F10': 'F11', 'F12': 'F12', 'F11': 'F6', 'F3': 'F13', 'F5': 'F9'}

{'C2': 'C2', 'C1': 'C1'}

High

{'C2': 'Low', 'C1': 'High'}

KNeighborsClassifier

C1

Suspicious Bidding Identification

With a higher degree of confidence, the classifier assigns the label C1 due to the fact that there is a close to zero chance that C2 is the label. The confidence level with respect to this classification output is largely due to the strong positive influence of F6. However, decreasing the probability that C1 is the true label are the negative features F8, F3, F4, F2, F5, and F1. Furthermore, F7 and F9 also increase the likelihood of C1 being the true label. In conclusion, the joint impact of the negative features is very weak compared to the positive features, hence the strong driving force of the classifier to assign the chosen label, C1.

{'C1': '99.90%', 'C2': '0.10%'}

{'F6': 'Z3', 'F8': 'Z9', 'F3': 'Z8', 'F4': 'Z1', 'F9': 'Z5', 'F7': 'Z4', 'F2': 'Z2', 'F5': 'Z6', 'F1': 'Z7'}

{'F3': 'F6', 'F9': 'F8', 'F8': 'F3', 'F1': 'F4', 'F5': 'F9', 'F4': 'F7', 'F2': 'F2', 'F6': 'F5', 'F7': 'F1'}

{'C2': 'C1', 'C1': 'C2'}

Normal

{'C1': 'Normal', 'C2': 'Suspicious'}

MLPClassifier

C1

Annual Income Earnings

According to the input variables, there is a 99.81% chance that C1 is the correct label for the given data instance, with a prediction probability of the alternative label, C2, equal to 0.19% which shows that there is little chance that C2 is the true label. F6, F2, and F11 are the top contributing features leading to the classification decision here. On the contrary, the F1, F9, and F4 are the least relevant features. The input features with moderate influence are F14, F5, F13, F12, F10, F8, and F3. Even though the different features have some level of influence on the classification, not all of them positively contribute. Actually, F13, F7, F3, and F9 have negative attributions, decreasing the classifier's response towards assigning C1; however, the joint influence of these features is outweighed by the positive attributions of F6, F2, F11, F14, and F5.

{'C1': '99.81%', 'C2': '0.19%'}

{'F6': 'Capital Gain', 'F2': 'Marital Status', 'F11': 'Capital Loss', 'F14': 'Age', 'F5': 'Hours per week', 'F12': 'Education', 'F13': 'Occupation', 'F7': 'Country', 'F10': 'Relationship', 'F8': 'Workclass', 'F3': 'Sex', 'F1': 'fnlwgt', 'F9': 'Education-Num', 'F4': 'Race'}

{'F11': 'F6', 'F6': 'F2', 'F12': 'F11', 'F1': 'F14', 'F13': 'F5', 'F4': 'F12', 'F7': 'F13', 'F14': 'F7', 'F8': 'F10', 'F2': 'F8', 'F10': 'F3', 'F3': 'F1', 'F5': 'F9', 'F9': 'F4'}

{'C2': 'C1', 'C1': 'C2'}

Under 50K

{'C1': 'Under 50K', 'C2': 'Above 50K'}

SVC

C2

Australian Credit Approval

With a confidence level equal to 81.43%, the classification algorithm labels the given data as C2, however, there is about an 18.57% chance that C1 could be the right label. The assignment of C2 to the given case is mainly based on the positive influence and contribution of input features F9, F13, and F2. Furthermore, the majority of the remaining input features have positive contributions, further increasing the predictability of label C2. F6, F5, F12, and F8 are the features with negative contributions, shifting the decision towards C1 instead of C2. Summarizing, comparing the attributions of the negative features to even those of the top three positive features explains why the algorithm is certain that C2 is the right label here.

{'C1': '18.57%', 'C2': '81.43%'}

{'F9': 'A8', 'F13': 'A9', 'F2': 'A14', 'F3': 'A12', 'F1': 'A7', 'F7': 'A4', 'F10': 'A5', 'F14': 'A11', 'F6': 'A1', 'F5': 'A13', 'F4': 'A10', 'F12': 'A2', 'F11': 'A6', 'F8': 'A3'}

{'F8': 'F9', 'F9': 'F13', 'F14': 'F2', 'F12': 'F3', 'F7': 'F1', 'F4': 'F7', 'F5': 'F10', 'F11': 'F14', 'F1': 'F6', 'F13': 'F5', 'F10': 'F4', 'F2': 'F12', 'F6': 'F11', 'F3': 'F8'}

{'C1': 'C1', 'C2': 'C2'}

Class 2

{'C1': 'Class 1', 'C2': 'Class 2'}

DNN

C2

Broadband Sevice Signup

The classification model employed here is very certain that the correct label is C2, implying that there is a near-zero chance that C1 is the label. The top six variables with the most influence on the prediction are all shifting the prediction in favour of C2. This might explain why the model is very certain about the C2 label and these top positive attributes are F2, F10, F36, F5, F8, and F18. F31, F9, F35, F41, and F4 all have moderately low-negative contributions, weakly swinging the direction of the model's decision towards C1. Finally, the decision to label the case as C2 is marginally supported by F38 and F16, whereas F30, F26, and F28 suggest that C1 could be the true label. In conclusion, the very high confidence level with regard to this prediction can be explained away by considering the very strong positive influence of F2, F10, F5, and F36.

{'C1': '0.00%', 'C2': '100.00%'}

{'F2': 'X38', 'F10': 'X5', 'F36': 'X33', 'F5': 'X37', 'F8': 'X27', 'F18': 'X3', 'F37': 'X16', 'F31': 'X41', 'F9': 'X2', 'F35': 'X39', 'F41': 'X29', 'F19': 'X25', 'F40': 'X1', 'F1': 'X19', 'F22': 'X10', 'F11': 'X18', 'F4': 'X26', 'F7': 'X35', 'F12': 'X40', 'F29': 'X24', 'F15': 'X32', 'F25': 'X22', 'F6': 'X21', 'F33': 'X6', 'F34': 'X14', 'F39': 'X42', 'F24': 'X30', 'F23': 'X28', 'F13': 'X34', 'F42': 'X23', 'F17': 'X9', 'F27': 'X20', 'F3': 'X11', 'F14': 'X12', 'F32': 'X8', 'F38': 'X15', 'F16': 'X31', 'F30': 'X17', 'F26': 'X13', 'F28': 'X7', 'F21': 'X36', 'F20': 'X4'}

{'F35': 'F2', 'F41': 'F10', 'F30': 'F36', 'F34': 'F5', 'F25': 'F8', 'F2': 'F18', 'F14': 'F37', 'F39': 'F31', 'F1': 'F9', 'F36': 'F35', 'F42': 'F41', 'F23': 'F19', 'F40': 'F40', 'F17': 'F1', 'F8': 'F22', 'F16': 'F11', 'F24': 'F4', 'F32': 'F7', 'F37': 'F12', 'F22': 'F29', 'F29': 'F15', 'F20': 'F25', 'F19': 'F6', 'F4': 'F33', 'F12': 'F34', 'F38': 'F39', 'F27': 'F24', 'F26': 'F23', 'F31': 'F13', 'F21': 'F42', 'F7': 'F17', 'F18': 'F27', 'F9': 'F3', 'F10': 'F14', 'F6': 'F32', 'F13': 'F38', 'F28': 'F16', 'F15': 'F30', 'F11': 'F26', 'F5': 'F28', 'F33': 'F21', 'F3': 'F20'}

{'C2': 'C1', 'C1': 'C2'}

Yes

{'C1': 'No', 'C2': 'Yes'}

KNeighborsClassifier

C2

Water Quality Classification

For the given case, the model generates the label C2 instead of C1, since C2 has a higher prediction likelihood than C1. According to the attribution graph shown, F3, and F6 are the most influential variables, resulting in the classification verdict above. F7, F1, and F9, on the other hand, are the least important variables considered by the model. F8, F2, F4, and F5 are shown to have a moderate influence on the classification made here. To sum up, with F7, F1, and F9 being the only variables contributing negatively, it is foreseeable why the model is quite certain that C1 is not the correct label for the given case.

{'C2': '87.50%', 'C1': '12.50%'}

{'F3': 'Hardness', 'F6': 'Sulfate', 'F8': 'Solids', 'F2': 'ph', 'F4': 'Organic_carbon', 'F5': 'Conductivity', 'F7': 'Trihalomethanes', 'F1': 'Turbidity', 'F9': 'Chloramines'}

{'F2': 'F3', 'F5': 'F6', 'F3': 'F8', 'F1': 'F2', 'F7': 'F4', 'F6': 'F5', 'F8': 'F7', 'F9': 'F1', 'F4': 'F9'}

{'C2': 'C2', 'C1': 'C1'}

Not Portable

{'C2': 'Not Portable', 'C1': 'Portable'}

DecisionTreeClassifier

C1

Concrete Strength Classification

Per the classifier, the most probable class with a very high confidence level is C1 mainly because the probability that C2 is the correct label is zero. From the attributions analysis, all the inputs are shown to contribute to or influence the above classification. The ranking of the features from the least important to the most important based on their degree of influence is as follows: F1, F4, F6, F2, F3, F8, F5, F7. Simply looking at the attributions of the input features, it is obvious why the classifier is very confident that C2 is not the correct label for the given All the features have positive contributions, resulting in a strong push towards C1.

{'C1': '100.00%', 'C2': '0.00%'}

{'F7': 'age_days', 'F5': 'superplasticizer', 'F8': 'cement', 'F3': 'coarseaggregate', 'F2': 'fineaggregate', 'F6': 'water', 'F4': 'slag', 'F1': 'flyash'}

{'F8': 'F7', 'F5': 'F5', 'F1': 'F8', 'F6': 'F3', 'F7': 'F2', 'F4': 'F6', 'F2': 'F4', 'F3': 'F1'}

{'C1': 'C1', 'C2': 'C2'}

Weak

{'C1': 'Weak', 'C2': 'Strong'}

KNeighborsClassifier

C1

Air Quality Prediction

The model predicted the C1 class for the test case with a very high degree of confidence. F4 is the only feature contributing against the prediction of the C1 class, while F6 and F2 contributed positively towards the prediction of C1. In decreasing order, F1, F5 and F3 were the three features with the least positive impact on the prediction of C1. Overall, given that only F4 has negative influence on the decision, it is not surprising to see the associated confidence level of the assigned label.

{'C4': '0.00%', 'C1': '100.00%', 'C3': '0.00%', 'C2': '0.00%'}

{'F4': 'MQ6', 'F6': 'MQ4', 'F2': 'MQ5', 'F1': 'MQ2', 'F5': 'MQ1', 'F3': 'MQ3'}

{'F6': 'F4', 'F4': 'F6', 'F5': 'F2', 'F2': 'F1', 'F1': 'F5', 'F3': 'F3'}

{'C2': 'C4', 'C3': 'C1', 'C1': 'C3', 'C4': 'C2'}

Presence of smoke

{'C4': 'Preparing meals', 'C1': 'Presence of smoke', 'C3': 'Cleaning', 'C2': 'Other'}

SVM_linear

C2

Mobile Price-Range Classification

Per the classification algorithm, the most probable class is C2 since the prediction probabilities indicate 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. This labelling is primarily owing to the roles that the features F17, F11, and F9 performed. On the lower end of the spectrum are the input features F16, F6, F15, and F14, which are demonstrated to be less essential for this labelling assignment task. Finally, only F12 and F20 are features having a negative effect, reducing the likelihood of C2 being the accurate classification here.

{'C1': '0.00%', 'C4': '0.00%', 'C3': '0.00%', 'C2': '100.00%'}

{'F11': 'ram', 'F9': 'battery_power', 'F17': 'px_width', 'F20': 'int_memory', 'F12': 'sc_h', 'F13': 'pc', 'F4': 'mobile_wt', 'F3': 'fc', 'F2': 'n_cores', 'F5': 'clock_speed', 'F10': 'blue', 'F8': 'three_g', 'F18': 'touch_screen', 'F19': 'm_dep', 'F7': 'px_height', 'F1': 'talk_time', 'F15': 'dual_sim', 'F14': 'wifi', 'F16': 'four_g', 'F6': 'sc_w'}

{'F11': 'F11', 'F1': 'F9', 'F10': 'F17', 'F4': 'F20', 'F12': 'F12', 'F8': 'F13', 'F6': 'F4', 'F3': 'F3', 'F7': 'F2', 'F2': 'F5', 'F15': 'F10', 'F18': 'F8', 'F19': 'F18', 'F5': 'F19', 'F9': 'F7', 'F14': 'F1', 'F16': 'F15', 'F20': 'F14', 'F17': 'F16', 'F13': 'F6'}

{'C1': 'C1', 'C3': 'C4', 'C4': 'C3', 'C2': 'C2'}

r4

{'C1': 'r1', 'C4': 'r2', 'C3': 'r3', 'C2': 'r4'}

GradientBoostingClassifier

C2

Australian Credit Approval

The predicted label is C2 at a confidence level of 92.11%, insinuating that there is a 7.89% chance that the label could be C1. In this case, the feature with the most significant influence on the model's decision is F12, with a very strong positive contribution in support of the C2 prediction. The next set of features with moderately high impact is F5, F13, F11, F4, and F7. Among this set, only F7 and F5 have a negative influence in support of label C1. Finally, on the lower end, the values of F6, F9, and F10 are deemed less important by the model when labelling this case.

{'C1': '7.89%', 'C2': '92.11%'}

{'F12': 'A8', 'F5': 'A14', 'F13': 'A4', 'F11': 'A7', 'F4': 'A13', 'F7': 'A9', 'F2': 'A2', 'F1': 'A3', 'F14': 'A10', 'F8': 'A5', 'F3': 'A1', 'F6': 'A11', 'F9': 'A12', 'F10': 'A6'}

{'F8': 'F12', 'F14': 'F5', 'F4': 'F13', 'F7': 'F11', 'F13': 'F4', 'F9': 'F7', 'F2': 'F2', 'F3': 'F1', 'F10': 'F14', 'F5': 'F8', 'F1': 'F3', 'F11': 'F6', 'F12': 'F9', 'F6': 'F10'}

{'C2': 'C1', 'C1': 'C2'}

Class 2

{'C1': 'Class 1', 'C2': 'Class 2'}

SVM

C2

Customer Churn Modelling

Taking into account the values of the input features, the prediction model's output for the case under consideration is C2. Given that there is a 27.27% probability that it could be C1, this labelling decision is not 100.0% certain. For the case under consideration, the label assignment is mainly due to the values of F2, F7, F6, and F9. F9 is identified as the most important or relevant, while F8 is considered the least important, since its contribution to the model is only marginal. In terms of the influence direction of each feature, F9 and F7 have a very strong positive contribution, driving the prediction higher toward the C2 class followed by F2, F6, and F4 all with moderately positive influence, whereas F8 has a negligible positive impact on the model in this case. Finally, for this case, F3, F1, F10, and F5 all have a negative impact on the prediction verdict, however, their pull or influence is not enough to transfer predictions in the direction of another class label, C1.

{'C1': '27.27%', 'C2': '72.73%'}

{'F9': 'Age', 'F7': 'IsActiveMember', 'F2': 'Geography', 'F6': 'NumOfProducts', 'F4': 'Gender', 'F3': 'Tenure', 'F1': 'CreditScore', 'F10': 'EstimatedSalary', 'F5': 'Balance', 'F8': 'HasCrCard'}

{'F4': 'F9', 'F9': 'F7', 'F2': 'F2', 'F7': 'F6', 'F3': 'F4', 'F5': 'F3', 'F1': 'F1', 'F10': 'F10', 'F6': 'F5', 'F8': 'F8'}

{'C1': 'C1', 'C2': 'C2'}

Leave

{'C1': 'Stay', 'C2': 'Leave'}

SVMClassifier_liner

C1

Employee Attrition

The prediction output decision by the model is that the likelihood of label C1 is 94.15% and that of class C2 is only around 5.85%, meaning the model is certain that C1 is likely the true label for the given case. First of all, the classification is performed with negligible contributions from the variables F26, F16, F5, F22, and F11 since their attributions are very close to zero. However, examination or inspection of the attributions of the different variables reveals that F19, F28, F27, F3, and F24 are the highly influential ones driving the predicted probabilities across the classes. In addition, the decision about the correct label for this case is moderately influenced by the values of F15, F23, F9, F13, F21, F29, and F20. In terms of the direction of influence or contributions of the variables, F19, F27, F3, F23, and F9 are the top positive variables, encouraging the predicted output to be equal to C1. Pushing the decision towards the C1 label and further away from C2 are the contriutions of the variables F21, F29, F30, and F14. Finally, the 5.85% likelihood of C2 can be attributed to the negative contributions of the top negative variables F28, F24, F14, F13, and F20.

{'C1': '94.15%', 'C2': '5.85%'}

{'F19': 'OverTime', 'F28': 'MaritalStatus', 'F27': 'NumCompaniesWorked', 'F3': 'BusinessTravel', 'F24': 'TotalWorkingYears', 'F15': 'DistanceFromHome', 'F23': 'YearsSinceLastPromotion', 'F9': 'Department', 'F13': 'Gender', 'F21': 'EnvironmentSatisfaction', 'F29': 'PerformanceRating', 'F20': 'Education', 'F30': 'JobRole', 'F14': 'YearsAtCompany', 'F8': 'JobInvolvement', 'F4': 'EducationField', 'F12': 'JobSatisfaction', 'F1': 'TrainingTimesLastYear', 'F17': 'HourlyRate', 'F10': 'WorkLifeBalance', 'F25': 'Age', 'F26': 'RelationshipSatisfaction', 'F16': 'DailyRate', 'F5': 'YearsInCurrentRole', 'F11': 'StockOptionLevel', 'F22': 'PercentSalaryHike', 'F6': 'MonthlyRate', 'F7': 'MonthlyIncome', 'F18': 'JobLevel', 'F2': 'YearsWithCurrManager'}

{'F26': 'F19', 'F25': 'F28', 'F8': 'F27', 'F17': 'F3', 'F11': 'F24', 'F3': 'F15', 'F15': 'F23', 'F21': 'F9', 'F23': 'F13', 'F28': 'F21', 'F19': 'F29', 'F27': 'F20', 'F24': 'F30', 'F13': 'F14', 'F29': 'F8', 'F22': 'F4', 'F30': 'F12', 'F12': 'F1', 'F4': 'F17', 'F20': 'F10', 'F1': 'F25', 'F18': 'F26', 'F2': 'F16', 'F14': 'F5', 'F10': 'F11', 'F9': 'F22', 'F7': 'F6', 'F6': 'F7', 'F5': 'F18', 'F16': 'F2'}

{'C2': 'C1', 'C1': 'C2'}

Stay

{'C1': 'Leave', 'C2': 'Leave'}

RandomForestClassifier

C2

Printer Sales

The most probable label, according to the classifier for the given data, is C2, which happens to have a higher predicted probability than that of C1. The major players in the above prediction output are F25, F15, F3, and F9. Conversely, F5, F23, F6, F7, F19, and F11 have negligible contributions when it comes to the classifier labelling the given case. Features such as F10, F17, F18, and F24 have a moderate influence on the decision. Comparing the stronger positive attributions of F15, F25, F17, and F18 to the negative attributions of F3, F9, F10, F13, F2, and F26 could explain why the classifier is quite confident in the label choice above.

{'C1': '20.00%', 'C2': '80.00%'}

{'F25': 'X24', 'F15': 'X1', 'F3': 'X8', 'F9': 'X21', 'F10': 'X4', 'F17': 'X10', 'F18': 'X3', 'F24': 'X15', 'F12': 'X9', 'F14': 'X23', 'F8': 'X25', 'F22': 'X7', 'F13': 'X22', 'F20': 'X11', 'F2': 'X17', 'F21': 'X18', 'F1': 'X26', 'F26': 'X13', 'F16': 'X6', 'F4': 'X20', 'F5': 'X16', 'F23': 'X19', 'F6': 'X2', 'F7': 'X12', 'F19': 'X5', 'F11': 'X14'}

{'F24': 'F25', 'F1': 'F15', 'F8': 'F3', 'F21': 'F9', 'F4': 'F10', 'F10': 'F17', 'F3': 'F18', 'F15': 'F24', 'F9': 'F12', 'F23': 'F14', 'F25': 'F8', 'F7': 'F22', 'F22': 'F13', 'F11': 'F20', 'F17': 'F2', 'F18': 'F21', 'F26': 'F1', 'F13': 'F26', 'F6': 'F16', 'F20': 'F4', 'F16': 'F5', 'F19': 'F23', 'F2': 'F6', 'F12': 'F7', 'F5': 'F19', 'F14': 'F11'}

{'C2': 'C1', 'C1': 'C2'}

More

{'C1': 'Less', 'C2': 'More'}

AdaBoostClassifier

C1

Air Quality Prediction

The most likely label is C1 since there is a 30.83% chance it could be C4, a 35.74% chance it could be C1, and a 33.42% chance it could be C3. Therefore, the correct label is not C2, which the model is very certain about. The above decision is primarily controlled by the values F1, F5, F2, and F4 which are shown to have positive influences that support the model's classification judgement here. In contrast, the remaining features F6 and F3 negatively support the classification decision, decreasing the chances of C1 being the correct label. In view of the fact that the probability distributions across the classes, we can conclude that the model is very uncertain about which label is appropriate for the given data instance and the features F6 and F3 should be blamed for this.

{'C4': '30.83%', 'C1': '35.74%', 'C2': '0.00%', 'C3': '33.42%'}

{'F1': 'MQ5', 'F5': 'MQ3', 'F2': 'MQ2', 'F4': 'MQ6', 'F6': 'MQ1', 'F3': 'MQ4'}

{'F5': 'F1', 'F3': 'F5', 'F2': 'F2', 'F6': 'F4', 'F1': 'F6', 'F4': 'F3'}

{'C1': 'C4', 'C4': 'C1', 'C2': 'C2', 'C3': 'C3'}

Presence of smoke

{'C4': 'Preparing meals', 'C1': 'Presence of smoke', 'C2': 'Cleaning', 'C3': 'Other'}

KNeighborsClassifier

C2

Ethereum Fraud Detection

Because the prediction probability of C1 is equal to 0.0%, the presented case is labelled as C2 with a very high level of confidence. For this classification scenario, the input features that have the greatest influence on the end outcome are F36, F8, F22, and F3. F13, F20, F26, F38, and F21 have a mild impact. However, because F17, F33, F16, and F4 have insignificant attribution values, they have little influence on the model's judgement. Among the top features, F36, F8, F22, and F3, only F36 and F22 exhibit negative attributions that favour the least likely class, C1, whereas F8 and F3 positively support the model's classification result for the provided data. Finally, only F35 and F32 positively contribute to the model's decision among the remaining significant features: F32, F18, F23, F35, F28, F25, and F7.

{'C1': '0.00%', 'C2': '100.00%'}

{'F36': 'Time Diff between first and last (Mins)', 'F8': 'Unique Received From Addresses', 'F22': 'Avg min between received tnx', 'F3': 'min val sent', 'F13': ' ERC20 min val rec', 'F20': 'Sent tnx', 'F26': 'min value received', 'F38': 'avg val sent', 'F21': ' ERC20 uniq rec addr', 'F2': ' ERC20 avg val sent', 'F37': ' ERC20 uniq rec contract addr', 'F1': ' ERC20 uniq rec token name', 'F6': 'max val sent', 'F32': 'Unique Sent To Addresses', 'F23': 'total transactions (including tnx to create contract', 'F18': 'avg val received', 'F35': ' ERC20 uniq sent addr.1', 'F28': ' ERC20 uniq sent token name', 'F25': ' Total ERC20 tnxs', 'F7': 'Received Tnx', 'F17': ' ERC20 uniq sent addr', 'F33': ' ERC20 max val sent', 'F16': ' ERC20 min val sent', 'F4': ' ERC20 avg val rec', 'F27': ' ERC20 max val rec', 'F11': 'Avg min between sent tnx', 'F9': ' ERC20 total Ether sent contract', 'F30': ' ERC20 total ether sent', 'F34': ' ERC20 total Ether received', 'F14': 'total ether balance', 'F31': 'total ether sent contracts', 'F12': 'total Ether sent', 'F5': 'avg value sent to contract', 'F29': 'max val sent to contract', 'F24': 'min value sent to contract', 'F19': 'max value received ', 'F10': 'Number of Created Contracts', 'F15': 'total ether received'}

{'F3': 'F36', 'F7': 'F8', 'F2': 'F22', 'F12': 'F3', 'F31': 'F13', 'F4': 'F20', 'F9': 'F26', 'F14': 'F38', 'F28': 'F21', 'F36': 'F2', 'F30': 'F37', 'F38': 'F1', 'F13': 'F6', 'F8': 'F32', 'F18': 'F23', 'F11': 'F18', 'F29': 'F35', 'F37': 'F28', 'F23': 'F25', 'F5': 'F7', 'F27': 'F17', 'F35': 'F33', 'F34': 'F16', 'F33': 'F4', 'F32': 'F27', 'F1': 'F11', 'F26': 'F9', 'F25': 'F30', 'F24': 'F34', 'F22': 'F14', 'F21': 'F31', 'F19': 'F12', 'F17': 'F5', 'F16': 'F29', 'F15': 'F24', 'F10': 'F19', 'F6': 'F10', 'F20': 'F15'}

{'C2': 'C1', 'C1': 'C2'}

Fraud

{'C1': 'Not Fraud', 'C2': 'Fraud'}

RandomForestClassifier

C1

Mobile Price-Range Classification

Between the four possible classes, the label for this case is predicted as C1, with a 73.08% likelihood that this is correct. With a likelihood of about 26.92%, the next probable label is shown to be C4. The prediction assessment above is mainly based on the values of the features F12, F6, F7, F17, and F14. The strongest impact came from F12, followed by F7, F6, F14, and F17. The collective contributions of the positive features F12, F6, F11, and F16 far outweigh the contributions of the negative attributes F7, F14, F17, and F15. Of the twenty attributes, majority of them are shown to have values pushing the prediction towards one of the three other possible classes and as such, it is surprising to see that the model is not 100% confident in the C1 prediction. On the grounds that the likelihood of C1 being correct is 73.08%, we can conclude that the model is quite confident with its final decision for the case under consideration.

{'C1': '73.08%', 'C4': '26.92%', 'C3': '0.00%', 'C2': '0.00%'}

{'F12': 'ram', 'F7': 'px_width', 'F6': 'battery_power', 'F14': 'px_height', 'F17': 'n_cores', 'F11': 'dual_sim', 'F16': 'touch_screen', 'F15': 'int_memory', 'F10': 'wifi', 'F1': 'fc', 'F13': 'four_g', 'F3': 'm_dep', 'F20': 'pc', 'F4': 'mobile_wt', 'F2': 'talk_time', 'F19': 'three_g', 'F18': 'sc_h', 'F5': 'sc_w', 'F9': 'blue', 'F8': 'clock_speed'}

{'F11': 'F12', 'F10': 'F7', 'F1': 'F6', 'F9': 'F14', 'F7': 'F17', 'F16': 'F11', 'F19': 'F16', 'F4': 'F15', 'F20': 'F10', 'F3': 'F1', 'F17': 'F13', 'F5': 'F3', 'F8': 'F20', 'F6': 'F4', 'F14': 'F2', 'F18': 'F19', 'F12': 'F18', 'F13': 'F5', 'F15': 'F9', 'F2': 'F8'}

{'C4': 'C1', 'C1': 'C4', 'C2': 'C3', 'C3': 'C2'}

r1

{'C1': 'r1', 'C4': 'r2', 'C3': 'r3', 'C2': 'r4'}