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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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model_file: example.pkl |
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widget: |
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structuredData: |
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'Unnamed: 32': |
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- .nan |
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- .nan |
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- .nan |
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area_mean: |
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- 481.9 |
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- 1130.0 |
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- 748.9 |
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area_se: |
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- 30.29 |
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- 96.05 |
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- 48.31 |
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area_worst: |
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- 677.9 |
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- 1866.0 |
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- 1156.0 |
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compactness_mean: |
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- 0.1058 |
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- 0.1029 |
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- 0.1223 |
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compactness_se: |
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- 0.01911 |
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- 0.01652 |
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- 0.01484 |
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compactness_worst: |
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- 0.2378 |
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- 0.2336 |
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- 0.2394 |
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concave points_mean: |
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- 0.03821 |
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- 0.07951 |
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- 0.08087 |
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concave points_se: |
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- 0.01037 |
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- 0.0137 |
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- 0.01093 |
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concave points_worst: |
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- 0.1015 |
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- 0.1789 |
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- 0.1514 |
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concavity_mean: |
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- 0.08005 |
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- 0.108 |
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- 0.1466 |
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concavity_se: |
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- 0.02701 |
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- 0.02269 |
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- 0.02813 |
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concavity_worst: |
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- 0.2671 |
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- 0.2687 |
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- 0.3791 |
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fractal_dimension_mean: |
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- 0.06373 |
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- 0.05461 |
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- 0.05796 |
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fractal_dimension_se: |
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- 0.003586 |
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- 0.001698 |
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- 0.002461 |
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fractal_dimension_worst: |
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- 0.0875 |
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- 0.06589 |
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- 0.08019 |
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id: |
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- 87930 |
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- 859575 |
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- 8670 |
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perimeter_mean: |
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- 81.09 |
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- 123.6 |
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- 101.7 |
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perimeter_se: |
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- 2.497 |
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- 5.486 |
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- 3.094 |
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perimeter_worst: |
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- 96.05 |
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- 165.9 |
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- 124.9 |
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radius_mean: |
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- 12.47 |
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- 18.94 |
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- 15.46 |
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radius_se: |
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- 0.3961 |
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- 0.7888 |
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- 0.4743 |
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radius_worst: |
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- 14.97 |
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- 24.86 |
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- 19.26 |
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smoothness_mean: |
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- 0.09965 |
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- 0.09009 |
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- 0.1092 |
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smoothness_se: |
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- 0.006953 |
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- 0.004444 |
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- 0.00624 |
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smoothness_worst: |
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- 0.1426 |
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- 0.1193 |
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- 0.1546 |
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symmetry_mean: |
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- 0.1925 |
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- 0.1582 |
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- 0.1931 |
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symmetry_se: |
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- 0.01782 |
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- 0.01386 |
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- 0.01397 |
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symmetry_worst: |
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- 0.3014 |
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- 0.2551 |
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- 0.2837 |
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texture_mean: |
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- 18.6 |
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- 21.31 |
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- 19.48 |
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texture_se: |
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- 1.044 |
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- 0.7975 |
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- 0.7859 |
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texture_worst: |
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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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[More Information Needed] |
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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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| memory | | |
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| steps | [('imputer', SimpleImputer()), ('scaler', StandardScaler()), ('model', LogisticRegression())] | |
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| verbose | False | |
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| imputer | SimpleImputer() | |
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| scaler | StandardScaler() | |
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| model | LogisticRegression() | |
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| imputer__add_indicator | False | |
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| imputer__copy | True | |
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| imputer__fill_value | | |
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| imputer__missing_values | nan | |
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| imputer__strategy | mean | |
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| imputer__verbose | 0 | |
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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-e60317e1-ee5c-4f4d-98a6-92332ba1474b {color: black;background-color: white;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b pre{padding: 0;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-toggleable {background-color: white;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b 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-e60317e1-ee5c-4f4d-98a6-92332ba1474b 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-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-estimator:hover {background-color: #d4ebff;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-item {z-index: 1;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-parallel-item:only-child::after {width: 0;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b 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-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b 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-e60317e1-ee5c-4f4d-98a6-92332ba1474b div.sk-text-repr-fallback {display: none;}</style><div id="sk-e60317e1-ee5c-4f4d-98a6-92332ba1474b" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('imputer', SimpleImputer()), ('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="6aee50d2-d0d7-437e-8e9b-bd1121de94e7" type="checkbox" ><label for="6aee50d2-d0d7-437e-8e9b-bd1121de94e7" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('imputer', SimpleImputer()), ('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="ac5b7f88-9a16-4c90-8fcb-2a4f833cadf1" type="checkbox" ><label for="ac5b7f88-9a16-4c90-8fcb-2a4f833cadf1" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="65ce6721-e323-4189-a9bd-e373e248f0f7" type="checkbox" ><label for="65ce6721-e323-4189-a9bd-e373e248f0f7" 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="2328c6c4-413e-46ed-b597-1b88227e45a5" type="checkbox" ><label for="2328c6c4-413e-46ed-b597-1b88227e45a5" 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 | 0.982456 | |
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| f1 score | 0.982456 | |
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# How to Get Started with the Model |
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[More Information Needed] |
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# Model Card Authors |
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This model card is written by following authors: |
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[More Information Needed] |
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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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[More Information Needed] |
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``` |
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# Confusion Matrix |
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