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--- |
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library_name: sklearn |
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tags: |
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- sklearn |
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- skops |
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- tabular-classification |
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model_file: model.pkl |
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widget: |
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structuredData: |
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area_mean: |
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- 407.4 |
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- 1335.0 |
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- 428.0 |
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area_se: |
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- 26.99 |
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- 77.02 |
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- 17.12 |
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area_worst: |
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- 508.9 |
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- 1946.0 |
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- 546.3 |
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compactness_mean: |
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- 0.05991 |
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- 0.1076 |
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- 0.069 |
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compactness_se: |
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- 0.01065 |
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- 0.01895 |
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- 0.01727 |
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compactness_worst: |
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- 0.1049 |
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- 0.3055 |
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- 0.188 |
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concave points_mean: |
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- 0.02069 |
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- 0.08941 |
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- 0.01393 |
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concave points_se: |
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- 0.009175 |
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- 0.01232 |
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- 0.006747 |
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concave points_worst: |
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- 0.06544 |
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- 0.2112 |
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- 0.06913 |
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concavity_mean: |
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- 0.02638 |
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- 0.1527 |
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- 0.02669 |
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concavity_se: |
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- 0.01245 |
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- 0.02681 |
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- 0.02045 |
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concavity_worst: |
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- 0.08105 |
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- 0.4159 |
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- 0.1471 |
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fractal_dimension_mean: |
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- 0.05934 |
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- 0.05478 |
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- 0.06057 |
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fractal_dimension_se: |
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- 0.001461 |
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- 0.001711 |
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- 0.002922 |
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fractal_dimension_worst: |
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- 0.06487 |
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- 0.07055 |
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- 0.07993 |
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perimeter_mean: |
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- 73.28 |
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- 134.8 |
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- 75.51 |
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perimeter_se: |
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- 2.684 |
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- 4.119 |
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- 1.444 |
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perimeter_worst: |
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- 83.12 |
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- 166.8 |
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- 85.22 |
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radius_mean: |
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- 11.5 |
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- 20.64 |
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- 11.84 |
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radius_se: |
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- 0.3927 |
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- 0.6137 |
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- 0.2222 |
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radius_worst: |
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- 12.97 |
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- 25.37 |
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- 13.3 |
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smoothness_mean: |
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- 0.09345 |
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- 0.09446 |
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- 0.08871 |
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smoothness_se: |
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- 0.00638 |
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- 0.006211 |
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- 0.005517 |
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smoothness_worst: |
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- 0.1183 |
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- 0.1562 |
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- 0.128 |
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symmetry_mean: |
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- 0.1834 |
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- 0.1571 |
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- 0.1533 |
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symmetry_se: |
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- 0.02292 |
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- 0.01276 |
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- 0.01616 |
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symmetry_worst: |
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- 0.274 |
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- 0.2689 |
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- 0.2535 |
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texture_mean: |
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- 18.45 |
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- 17.35 |
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- 18.94 |
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texture_se: |
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- 0.8429 |
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- 0.6575 |
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- 0.8652 |
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texture_worst: |
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- 22.46 |
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- 23.17 |
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- 24.99 |
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--- |
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# Model description |
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This is a Logistic Regression trained on breast cancer dataset. |
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## Intended uses & limitations |
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This model is trained for educational purposes. |
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## Training Procedure |
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### Hyperparameters |
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The model is trained with below hyperparameters. |
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<details> |
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<summary> Click to expand </summary> |
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| Hyperparameter | Value | |
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|--------------------------|-----------------------------------------------------------------| |
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| memory | | |
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| steps | [('scaler', StandardScaler()), ('model', LogisticRegression())] | |
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| verbose | False | |
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| scaler | StandardScaler() | |
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| model | LogisticRegression() | |
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| scaler__copy | True | |
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| scaler__with_mean | True | |
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| scaler__with_std | True | |
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| model__C | 1.0 | |
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| model__class_weight | | |
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| model__dual | False | |
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| model__fit_intercept | True | |
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| model__intercept_scaling | 1 | |
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| model__l1_ratio | | |
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| model__max_iter | 100 | |
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| model__multi_class | auto | |
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| model__n_jobs | | |
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| model__penalty | l2 | |
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| model__random_state | | |
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| model__solver | lbfgs | |
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| model__tol | 0.0001 | |
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| model__verbose | 0 | |
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| model__warm_start | False | |
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</details> |
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### Model Plot |
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The model plot is below. |
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<style>#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 {color: black;background-color: white;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 pre{padding: 0;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-toggleable {background-color: white;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-estimator:hover {background-color: #d4ebff;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-item {z-index: 1;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-parallel-item:only-child::after {width: 0;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152 div.sk-text-repr-fallback {display: none;}</style><div id="sk-5b6643ea-0cef-4d0c-8389-2cf071bf6152" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('scaler', StandardScaler()), ('model', LogisticRegression())])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="76a688ab-e260-4cf7-a9f2-bf77900be27c" type="checkbox" ><label for="76a688ab-e260-4cf7-a9f2-bf77900be27c" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('scaler', StandardScaler()), ('model', LogisticRegression())])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="6a4fcd10-6b63-40a6-a848-13717b9f7c82" type="checkbox" ><label for="6a4fcd10-6b63-40a6-a848-13717b9f7c82" class="sk-toggleable__label sk-toggleable__label-arrow">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="974bd93d-19db-4a61-b7ff-66d07e5bbadb" type="checkbox" ><label for="974bd93d-19db-4a61-b7ff-66d07e5bbadb" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div> |
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## Evaluation Results |
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You can find the details about evaluation process and the evaluation results. |
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| Metric | Value | |
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|----------|----------| |
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| accuracy | 0.965035 | |
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| f1 score | 0.965035 | |
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# How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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import joblib |
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import json |
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import pandas as pd |
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clf = joblib.load(model.pkl) |
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with open("config.json") as f: |
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config = json.load(f) |
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clf.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"])) |
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``` |
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# Additional Content |
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## Confusion Matrix |
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 |
