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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-regression |
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model_format: pickle |
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model_file: model.pkl |
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widget: |
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structuredData: |
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Fedu: |
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- 3 |
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- 3 |
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- 3 |
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Fjob: |
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- other |
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- other |
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- services |
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G1: |
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- 12 |
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- 13 |
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- 8 |
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G2: |
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- 13 |
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- 14 |
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- 7 |
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G3: |
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- 12 |
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- 14 |
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- 0 |
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Medu: |
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- 3 |
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- 2 |
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- 1 |
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Mjob: |
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- services |
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- other |
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- at_home |
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Pstatus: |
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- T |
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- T |
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- T |
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Walc: |
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- 2 |
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- 1 |
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- 1 |
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absences: |
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- 2 |
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- 0 |
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- 0 |
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activities: |
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- 'yes' |
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- 'no' |
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- 'yes' |
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address: |
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- U |
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- U |
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- U |
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age: |
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- 16 |
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- 16 |
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- 16 |
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failures: |
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- 0 |
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- 0 |
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- 3 |
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famrel: |
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- 4 |
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- 5 |
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- 4 |
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famsize: |
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- GT3 |
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- GT3 |
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- GT3 |
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famsup: |
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- 'no' |
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- 'no' |
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- 'no' |
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freetime: |
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- 2 |
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- 3 |
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- 3 |
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goout: |
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- 3 |
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- 3 |
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- 5 |
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guardian: |
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- mother |
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- father |
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- mother |
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health: |
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- 3 |
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- 3 |
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- 3 |
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higher: |
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- 'yes' |
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- 'yes' |
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- 'yes' |
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internet: |
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- 'yes' |
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- 'yes' |
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- 'yes' |
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nursery: |
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- 'yes' |
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- 'yes' |
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- 'no' |
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paid: |
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- 'yes' |
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- 'no' |
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- 'no' |
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reason: |
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- home |
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- home |
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- home |
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romantic: |
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- 'yes' |
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- 'no' |
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- 'yes' |
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school: |
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- GP |
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- GP |
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- GP |
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schoolsup: |
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- 'no' |
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- 'no' |
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- 'no' |
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sex: |
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- M |
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- M |
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- F |
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studytime: |
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- 2 |
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- 1 |
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- 2 |
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traveltime: |
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- 1 |
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- 2 |
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- 1 |
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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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[More Information Needed] |
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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 | [('onehotencoder', OneHotEncoder(handle_unknown='ignore', sparse=False)), ('xgbregressor', XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,<br /> colsample_bynode=1, colsample_bytree=1, enable_categorical=False,<br /> gamma=0, gpu_id=-1, importance_type=None,<br /> interaction_constraints='', learning_rate=0.300000012,<br /> max_delta_step=0, max_depth=5, min_child_weight=1, missing=nan,<br /> monotone_constraints='()', n_estimators=100, n_jobs=8,<br /> num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,<br /> reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',<br /> validate_parameters=1, verbosity=None))] | |
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| verbose | False | |
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| onehotencoder | OneHotEncoder(handle_unknown='ignore', sparse=False) | |
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| xgbregressor | XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,<br /> colsample_bynode=1, colsample_bytree=1, enable_categorical=False,<br /> gamma=0, gpu_id=-1, importance_type=None,<br /> interaction_constraints='', learning_rate=0.300000012,<br /> max_delta_step=0, max_depth=5, min_child_weight=1, missing=nan,<br /> monotone_constraints='()', n_estimators=100, n_jobs=8,<br /> num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,<br /> reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',<br /> validate_parameters=1, verbosity=None) | |
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| onehotencoder__categories | auto | |
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| onehotencoder__drop | | |
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| onehotencoder__dtype | <class 'numpy.float64'> | |
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| onehotencoder__handle_unknown | ignore | |
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| onehotencoder__max_categories | | |
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| onehotencoder__min_frequency | | |
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| onehotencoder__sparse | False | |
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| xgbregressor__objective | reg:squarederror | |
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| xgbregressor__base_score | 0.5 | |
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| xgbregressor__booster | gbtree | |
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| xgbregressor__colsample_bylevel | 1 | |
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| xgbregressor__colsample_bynode | 1 | |
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| xgbregressor__colsample_bytree | 1 | |
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| xgbregressor__enable_categorical | False | |
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| xgbregressor__gamma | 0 | |
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| xgbregressor__gpu_id | -1 | |
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| xgbregressor__importance_type | | |
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| xgbregressor__interaction_constraints | | |
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| xgbregressor__learning_rate | 0.300000012 | |
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| xgbregressor__max_delta_step | 0 | |
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| xgbregressor__max_depth | 5 | |
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| xgbregressor__min_child_weight | 1 | |
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| xgbregressor__missing | nan | |
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| xgbregressor__monotone_constraints | () | |
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| xgbregressor__n_estimators | 100 | |
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| xgbregressor__n_jobs | 8 | |
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| xgbregressor__num_parallel_tree | 1 | |
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| xgbregressor__predictor | auto | |
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| xgbregressor__random_state | 0 | |
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| xgbregressor__reg_alpha | 0 | |
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| xgbregressor__reg_lambda | 1 | |
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| xgbregressor__scale_pos_weight | 1 | |
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| xgbregressor__subsample | 1 | |
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| xgbregressor__tree_method | exact | |
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| xgbregressor__validate_parameters | 1 | |
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| xgbregressor__verbosity | | |
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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-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-container-id-1 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-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('onehotencoder',OneHotEncoder(handle_unknown='ignore', sparse=False)),('xgbregressor',XGBRegressor(base_score=0.5, booster='gbtree',colsample_bylevel=1, colsample_bynode=1,colsample_bytree=1, enable_categorical=False,gamma=0, gpu_id=-1, importance_type=None,interaction_constraints='',learning_rate=0.300000012, max_delta_step=0,max_depth=5, min_child_weight=1, missing=nan,monotone_constraints='()', n_estimators=100,n_jobs=8, num_parallel_tree=1, predictor='auto',random_state=0, reg_alpha=0, reg_lambda=1,scale_pos_weight=1, subsample=1,tree_method='exact', validate_parameters=1,verbosity=None))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</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="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('onehotencoder',OneHotEncoder(handle_unknown='ignore', sparse=False)),('xgbregressor',XGBRegressor(base_score=0.5, booster='gbtree',colsample_bylevel=1, colsample_bynode=1,colsample_bytree=1, enable_categorical=False,gamma=0, gpu_id=-1, importance_type=None,interaction_constraints='',learning_rate=0.300000012, max_delta_step=0,max_depth=5, min_child_weight=1, missing=nan,monotone_constraints='()', n_estimators=100,n_jobs=8, num_parallel_tree=1, predictor='auto',random_state=0, reg_alpha=0, reg_lambda=1,scale_pos_weight=1, subsample=1,tree_method='exact', validate_parameters=1,verbosity=None))])</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="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder(handle_unknown='ignore', sparse=False)</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">XGBRegressor</label><div class="sk-toggleable__content"><pre>XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,colsample_bynode=1, colsample_bytree=1, enable_categorical=False,gamma=0, gpu_id=-1, importance_type=None,interaction_constraints='', learning_rate=0.300000012,max_delta_step=0, max_depth=5, min_child_weight=1, missing=nan,monotone_constraints='()', n_estimators=100, n_jobs=8,num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',validate_parameters=1, verbosity=None)</pre></div></div></div></div></div></div></div> |
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## Evaluation Results |
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[More Information Needed] |
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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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