| | --- |
| | library_name: sklearn |
| | tags: |
| | - sklearn |
| | - skops |
| | - tabular-classification |
| | widget: |
| | structuredData: |
| | area error: |
| | - 30.29 |
| | - 96.05 |
| | - 48.31 |
| | compactness error: |
| | - 0.01911 |
| | - 0.01652 |
| | - 0.01484 |
| | concave points error: |
| | - 0.01037 |
| | - 0.0137 |
| | - 0.01093 |
| | concavity error: |
| | - 0.02701 |
| | - 0.02269 |
| | - 0.02813 |
| | fractal dimension error: |
| | - 0.003586 |
| | - 0.001698 |
| | - 0.002461 |
| | mean area: |
| | - 481.9 |
| | - 1130.0 |
| | - 748.9 |
| | mean compactness: |
| | - 0.1058 |
| | - 0.1029 |
| | - 0.1223 |
| | mean concave points: |
| | - 0.03821 |
| | - 0.07951 |
| | - 0.08087 |
| | mean concavity: |
| | - 0.08005 |
| | - 0.108 |
| | - 0.1466 |
| | mean fractal dimension: |
| | - 0.06373 |
| | - 0.05461 |
| | - 0.05796 |
| | mean perimeter: |
| | - 81.09 |
| | - 123.6 |
| | - 101.7 |
| | mean radius: |
| | - 12.47 |
| | - 18.94 |
| | - 15.46 |
| | mean smoothness: |
| | - 0.09965 |
| | - 0.09009 |
| | - 0.1092 |
| | mean symmetry: |
| | - 0.1925 |
| | - 0.1582 |
| | - 0.1931 |
| | mean texture: |
| | - 18.6 |
| | - 21.31 |
| | - 19.48 |
| | perimeter error: |
| | - 2.497 |
| | - 5.486 |
| | - 3.094 |
| | radius error: |
| | - 0.3961 |
| | - 0.7888 |
| | - 0.4743 |
| | smoothness error: |
| | - 0.006953 |
| | - 0.004444 |
| | - 0.00624 |
| | symmetry error: |
| | - 0.01782 |
| | - 0.01386 |
| | - 0.01397 |
| | texture error: |
| | - 1.044 |
| | - 0.7975 |
| | - 0.7859 |
| | worst area: |
| | - 677.9 |
| | - 1866.0 |
| | - 1156.0 |
| | worst compactness: |
| | - 0.2378 |
| | - 0.2336 |
| | - 0.2394 |
| | worst concave points: |
| | - 0.1015 |
| | - 0.1789 |
| | - 0.1514 |
| | worst concavity: |
| | - 0.2671 |
| | - 0.2687 |
| | - 0.3791 |
| | worst fractal dimension: |
| | - 0.0875 |
| | - 0.06589 |
| | - 0.08019 |
| | worst perimeter: |
| | - 96.05 |
| | - 165.9 |
| | - 124.9 |
| | worst radius: |
| | - 14.97 |
| | - 24.86 |
| | - 19.26 |
| | worst smoothness: |
| | - 0.1426 |
| | - 0.1193 |
| | - 0.1546 |
| | worst symmetry: |
| | - 0.3014 |
| | - 0.2551 |
| | - 0.2837 |
| | worst texture: |
| | - 24.64 |
| | - 26.58 |
| | - 26.0 |
| | --- |
| | |
| | # Model description |
| |
|
| | [More Information Needed] |
| |
|
| | ## Intended uses & limitations |
| |
|
| | [More Information Needed] |
| |
|
| | ## Training Procedure |
| |
|
| | ### Hyperparameters |
| |
|
| | The model is trained with below hyperparameters. |
| |
|
| | <details> |
| | <summary> Click to expand </summary> |
| |
|
| | | Hyperparameter | Value | |
| | |---------------------------------|----------------------------------------------------------| |
| | | aggressive_elimination | False | |
| | | cv | 5 | |
| | | error_score | nan | |
| | | estimator__categorical_features | | |
| | | estimator__early_stopping | auto | |
| | | estimator__l2_regularization | 0.0 | |
| | | estimator__learning_rate | 0.1 | |
| | | estimator__loss | auto | |
| | | estimator__max_bins | 255 | |
| | | estimator__max_depth | | |
| | | estimator__max_iter | 100 | |
| | | estimator__max_leaf_nodes | 31 | |
| | | estimator__min_samples_leaf | 20 | |
| | | estimator__monotonic_cst | | |
| | | estimator__n_iter_no_change | 10 | |
| | | estimator__random_state | | |
| | | estimator__scoring | loss | |
| | | estimator__tol | 1e-07 | |
| | | estimator__validation_fraction | 0.1 | |
| | | estimator__verbose | 0 | |
| | | estimator__warm_start | False | |
| | | estimator | HistGradientBoostingClassifier() | |
| | | factor | 3 | |
| | | max_resources | auto | |
| | | min_resources | exhaust | |
| | | n_jobs | -1 | |
| | | param_grid | {'max_leaf_nodes': [5, 10, 15], 'max_depth': [2, 5, 10]} | |
| | | random_state | 42 | |
| | | refit | True | |
| | | resource | n_samples | |
| | | return_train_score | True | |
| | | scoring | | |
| | | verbose | 0 | |
| |
|
| | </details> |
| |
|
| | ### Model Plot |
| |
|
| | The model plot is below. |
| |
|
| | <style>#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce {color: black;background-color: white;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce pre{padding: 0;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-toggleable {background-color: white;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce 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-3de79340-4ee5-4aee-9c89-b3b7696153ce 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-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-estimator:hover {background-color: #d4ebff;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-item {z-index: 1;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-parallel-item:only-child::after {width: 0;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce 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-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce 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-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-text-repr-fallback {display: none;}</style><div id="sk-3de79340-4ee5-4aee-9c89-b3b7696153ce" 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="474afc8c-e67d-430c-9432-eedced794614" type="checkbox" ><label for="474afc8c-e67d-430c-9432-eedced794614" 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="cf1d66b1-cfe8-40b1-b6e9-7a62640add17" type="checkbox" ><label for="cf1d66b1-cfe8-40b1-b6e9-7a62640add17" 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> |
| | |
| | ## Evaluation Results |
| | |
| | You can find the details about evaluation process and the evaluation results. |
| | |
| | |
| | |
| | | Metric | Value | |
| | |----------|---------| |
| | |
| | # How to Get Started with the Model |
| | |
| | Use the code below to get started with the model. |
| | |
| | <details> |
| | <summary> Click to expand </summary> |
| | |
| | ```python |
| | [More Information Needed] |
| | ``` |
| | |
| | </details> |
| | |
| | |
| | |
| | |
| | # Model Card Authors |
| | |
| | This model card is written by following authors: |
| | |
| | [More Information Needed] |
| | |
| | # Model Card Contact |
| | |
| | You can contact the model card authors through following channels: |
| | [More Information Needed] |
| | |
| | # Citation |
| | |
| | Below you can find information related to citation. |
| | |
| | **BibTeX:** |
| | ``` |
| | [More Information Needed] |
| | ``` |
