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--- |
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license: mit |
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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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widget: |
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structuredData: |
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area error: |
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- 30.29 |
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- 96.05 |
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- 48.31 |
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compactness error: |
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- 0.01911 |
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- 0.01652 |
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- 0.01484 |
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concave points error: |
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- 0.01037 |
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- 0.0137 |
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- 0.01093 |
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concavity error: |
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- 0.02701 |
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- 0.02269 |
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- 0.02813 |
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fractal dimension error: |
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- 0.003586 |
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- 0.001698 |
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- 0.002461 |
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mean area: |
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- 481.9 |
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- 1130.0 |
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- 748.9 |
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mean compactness: |
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- 0.1058 |
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- 0.1029 |
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- 0.1223 |
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mean concave points: |
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- 0.03821 |
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- 0.07951 |
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- 0.08087 |
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mean concavity: |
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- 0.08005 |
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- 0.108 |
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- 0.1466 |
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mean fractal dimension: |
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- 0.06373 |
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- 0.05461 |
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- 0.05796 |
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mean perimeter: |
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- 81.09 |
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- 123.6 |
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- 101.7 |
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mean radius: |
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- 12.47 |
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- 18.94 |
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- 15.46 |
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mean smoothness: |
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- 0.09965 |
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- 0.09009 |
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- 0.1092 |
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mean symmetry: |
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- 0.1925 |
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- 0.1582 |
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- 0.1931 |
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mean texture: |
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- 18.6 |
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- 21.31 |
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- 19.48 |
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perimeter error: |
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- 2.497 |
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- 5.486 |
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- 3.094 |
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radius error: |
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- 0.3961 |
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- 0.7888 |
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- 0.4743 |
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smoothness error: |
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- 0.006953 |
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- 0.004444 |
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- 0.00624 |
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symmetry error: |
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- 0.01782 |
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- 0.01386 |
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- 0.01397 |
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texture error: |
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- 1.044 |
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- 0.7975 |
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- 0.7859 |
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worst area: |
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- 677.9 |
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- 1866.0 |
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- 1156.0 |
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worst compactness: |
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- 0.2378 |
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- 0.2336 |
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- 0.2394 |
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worst concave points: |
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- 0.1015 |
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- 0.1789 |
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- 0.1514 |
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worst concavity: |
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- 0.2671 |
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- 0.2687 |
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- 0.3791 |
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worst fractal dimension: |
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- 0.0875 |
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- 0.06589 |
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- 0.08019 |
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worst perimeter: |
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- 96.05 |
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- 165.9 |
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- 124.9 |
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worst radius: |
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- 14.97 |
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- 24.86 |
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- 19.26 |
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worst smoothness: |
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- 0.1426 |
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- 0.1193 |
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- 0.1546 |
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worst symmetry: |
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- 0.3014 |
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- 0.2551 |
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- 0.2837 |
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worst texture: |
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- 24.64 |
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- 26.58 |
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- 26.0 |
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--- |
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# Model description |
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This is a HistGradientBoostingClassifier model trained on breast cancer dataset. It's trained with Halving Grid Search Cross Validation, with parameter grids on max_leaf_nodes and max_depth. |
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## Intended uses & limitations |
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This model is not ready to be used in production. |
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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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| aggressive_elimination | False | |
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| cv | 5 | |
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| error_score | nan | |
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| estimator__categorical_features | | |
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| estimator__early_stopping | auto | |
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| estimator__l2_regularization | 0.0 | |
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| estimator__learning_rate | 0.1 | |
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| estimator__loss | auto | |
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| estimator__max_bins | 255 | |
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| estimator__max_depth | | |
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| estimator__max_iter | 100 | |
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| estimator__max_leaf_nodes | 31 | |
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| estimator__min_samples_leaf | 20 | |
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| estimator__monotonic_cst | | |
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| estimator__n_iter_no_change | 10 | |
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| estimator__random_state | | |
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| estimator__scoring | loss | |
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| estimator__tol | 1e-07 | |
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| estimator__validation_fraction | 0.1 | |
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| estimator__verbose | 0 | |
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| estimator__warm_start | False | |
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| estimator | HistGradientBoostingClassifier() | |
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| factor | 3 | |
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| max_resources | auto | |
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| min_resources | exhaust | |
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| n_jobs | -1 | |
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| param_grid | {'max_leaf_nodes': [5, 10, 15], 'max_depth': [2, 5, 10]} | |
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| random_state | 42 | |
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| refit | True | |
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| resource | n_samples | |
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| return_train_score | True | |
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| scoring | | |
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| verbose | 0 | |
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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-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 {color: black;background-color: white;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 pre{padding: 0;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-toggleable {background-color: white;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 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-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 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-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-estimator:hover {background-color: #d4ebff;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-item {z-index: 1;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-parallel-item:only-child::after {width: 0;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 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-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 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-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-text-repr-fallback {display: none;}</style><div id="sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>HalvingGridSearchCV(estimator=HistGradientBoostingClassifier(), n_jobs=-1,param_grid={'max_depth': [2, 5, 10],'max_leaf_nodes': [5, 10, 15]},random_state=42)</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="ab167486-be7e-4eb5-be01-ba21adbd7469" type="checkbox" ><label for="ab167486-be7e-4eb5-be01-ba21adbd7469" class="sk-toggleable__label sk-toggleable__label-arrow">HalvingGridSearchCV</label><div class="sk-toggleable__content"><pre>HalvingGridSearchCV(estimator=HistGradientBoostingClassifier(), n_jobs=-1,param_grid={'max_depth': [2, 5, 10],'max_leaf_nodes': [5, 10, 15]},random_state=42)</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="e9df9f06-8d9e-4379-ad72-52f461408663" type="checkbox" ><label for="e9df9f06-8d9e-4379-ad72-52f461408663" class="sk-toggleable__label sk-toggleable__label-arrow">HistGradientBoostingClassifier</label><div class="sk-toggleable__content"><pre>HistGradientBoostingClassifier()</pre></div></div></div></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 | 0.959064 | |
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| f1 score | 0.959064 | |
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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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<details> |
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<summary> Click to expand </summary> |
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```python |
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import pickle |
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with open(pkl_filename, 'rb') as file: |
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clf = pickle.load(file) |
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``` |
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</details> |
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# Model Card Authors |
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This model card is written by following authors: |
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skops_user |
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# Model Card Contact |
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You can contact the model card authors through following channels: |
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[More Information Needed] |
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# Citation |
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Below you can find information related to citation. |
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**BibTeX:** |
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``` |
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bibtex |
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@inproceedings{...,year={2020}} |
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``` |
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# Additional Content |
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## Confusion matrix |
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## Hyperparameter search results |
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<details> |
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<summary> Click to expand </summary> |
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| iter | n_resources | mean_fit_time | std_fit_time | mean_score_time | std_score_time | param_max_depth | param_max_leaf_nodes | params | split0_test_score | split1_test_score | split2_test_score | split3_test_score | split4_test_score | mean_test_score | std_test_score | rank_test_score | split0_train_score | split1_train_score | split2_train_score | split3_train_score | split4_train_score | mean_train_score | std_train_score | |
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|--------|---------------|-----------------|----------------|-------------------|------------------|-------------------|------------------------|-----------------------------------------|---------------------|---------------------|---------------------|---------------------|---------------------|-------------------|------------------|-------------------|----------------------|----------------------|----------------------|----------------------|----------------------|--------------------|-------------------| |
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| 0 | 44 | 0.0498069 | 0.0107112 | 0.0121156 | 0.0061838 | 2 | 5 | {'max_depth': 2, 'max_leaf_nodes': 5} | 0.875 | 0.5 | 0.625 | 0.75 | 0.375 | 0.625 | 0.176777 | 5 | 0.628571 | 0.628571 | 0.628571 | 0.514286 | 0.514286 | 0.582857 | 0.0559883 | |
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| 0 | 44 | 0.0492636 | 0.0187271 | 0.00738611 | 0.00245441 | 2 | 10 | {'max_depth': 2, 'max_leaf_nodes': 10} | 0.875 | 0.5 | 0.625 | 0.75 | 0.375 | 0.625 | 0.176777 | 5 | 0.628571 | 0.628571 | 0.628571 | 0.514286 | 0.514286 | 0.582857 | 0.0559883 | |
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| 0 | 44 | 0.0572055 | 0.0153176 | 0.0111395 | 0.0010297 | 2 | 15 | {'max_depth': 2, 'max_leaf_nodes': 15} | 0.875 | 0.5 | 0.625 | 0.75 | 0.375 | 0.625 | 0.176777 | 5 | 0.628571 | 0.628571 | 0.628571 | 0.514286 | 0.514286 | 0.582857 | 0.0559883 | |
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| 0 | 44 | 0.0498482 | 0.0177091 | 0.00857358 | 0.00415935 | 5 | 5 | {'max_depth': 5, 'max_leaf_nodes': 5} | 0.875 | 0.5 | 0.625 | 0.75 | 0.375 | 0.625 | 0.176777 | 5 | 0.628571 | 0.628571 | 0.628571 | 0.514286 | 0.514286 | 0.582857 | 0.0559883 | |
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| 0 | 44 | 0.0500658 | 0.00992094 | 0.00998321 | 0.00527031 | 5 | 10 | {'max_depth': 5, 'max_leaf_nodes': 10} | 0.875 | 0.5 | 0.625 | 0.75 | 0.375 | 0.625 | 0.176777 | 5 | 0.628571 | 0.628571 | 0.628571 | 0.514286 | 0.514286 | 0.582857 | 0.0559883 | |
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| 0 | 44 | 0.0525903 | 0.0151616 | 0.00874681 | 0.00462998 | 5 | 15 | {'max_depth': 5, 'max_leaf_nodes': 15} | 0.875 | 0.5 | 0.625 | 0.75 | 0.375 | 0.625 | 0.176777 | 5 | 0.628571 | 0.628571 | 0.628571 | 0.514286 | 0.514286 | 0.582857 | 0.0559883 | |
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| 0 | 44 | 0.0512018 | 0.0130152 | 0.00881834 | 0.00500514 | 10 | 5 | {'max_depth': 10, 'max_leaf_nodes': 5} | 0.875 | 0.5 | 0.625 | 0.75 | 0.375 | 0.625 | 0.176777 | 5 | 0.628571 | 0.628571 | 0.628571 | 0.514286 | 0.514286 | 0.582857 | 0.0559883 | |
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| 0 | 44 | 0.0566921 | 0.0186051 | 0.00513492 | 0.000498488 | 10 | 10 | {'max_depth': 10, 'max_leaf_nodes': 10} | 0.875 | 0.5 | 0.625 | 0.75 | 0.375 | 0.625 | 0.176777 | 5 | 0.628571 | 0.628571 | 0.628571 | 0.514286 | 0.514286 | 0.582857 | 0.0559883 | |
|
| 0 | 44 | 0.060587 | 0.04041 | 0.00987453 | 0.00529624 | 10 | 15 | {'max_depth': 10, 'max_leaf_nodes': 15} | 0.875 | 0.5 | 0.625 | 0.75 | 0.375 | 0.625 | 0.176777 | 5 | 0.628571 | 0.628571 | 0.628571 | 0.514286 | 0.514286 | 0.582857 | 0.0559883 | |
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| 1 | 132 | 0.232459 | 0.0479878 | 0.0145514 | 0.00856422 | 10 | 5 | {'max_depth': 10, 'max_leaf_nodes': 5} | 0.961538 | 0.923077 | 0.923077 | 0.961538 | 0.961538 | 0.946154 | 0.0188422 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | |
|
| 1 | 132 | 0.272297 | 0.0228833 | 0.011561 | 0.0068272 | 10 | 10 | {'max_depth': 10, 'max_leaf_nodes': 10} | 0.961538 | 0.923077 | 0.923077 | 0.961538 | 0.961538 | 0.946154 | 0.0188422 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | |
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| 1 | 132 | 0.239161 | 0.0330412 | 0.0116591 | 0.003554 | 10 | 15 | {'max_depth': 10, 'max_leaf_nodes': 15} | 0.961538 | 0.923077 | 0.923077 | 0.961538 | 0.961538 | 0.946154 | 0.0188422 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | |
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| 2 | 396 | 0.920334 | 0.18198 | 0.0166654 | 0.00776263 | 10 | 15 | {'max_depth': 10, 'max_leaf_nodes': 15} | 0.962025 | 0.911392 | 0.987342 | 0.974359 | 0.935897 | 0.954203 | 0.0273257 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | |
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</details> |
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## Classification report |
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<details> |
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<summary> Click to expand </summary> |
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| index | precision | recall | f1-score | support | |
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|--------------|-------------|----------|------------|-----------| |
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| malignant | 0.951613 | 0.936508 | 0.944 | 63 | |
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| benign | 0.963303 | 0.972222 | 0.967742 | 108 | |
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| macro avg | 0.957458 | 0.954365 | 0.955871 | 171 | |
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| weighted avg | 0.958996 | 0.959064 | 0.958995 | 171 | |
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</details> |
