YAML Metadata
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Model description
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Intended uses & limitations
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Training Procedure
Hyperparameters
The model is trained with below hyperparameters.
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| Hyperparameter | Value |
|---|---|
| memory | |
| 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))] |
| verbose | False |
| 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) |
| onehotencoder__categories | auto |
| onehotencoder__drop | |
| onehotencoder__dtype | <class 'numpy.float64'> |
| onehotencoder__handle_unknown | ignore |
| onehotencoder__max_categories | |
| onehotencoder__min_frequency | |
| onehotencoder__sparse | False |
| xgbregressor__objective | reg:squarederror |
| xgbregressor__base_score | 0.5 |
| xgbregressor__booster | gbtree |
| xgbregressor__colsample_bylevel | 1 |
| xgbregressor__colsample_bynode | 1 |
| xgbregressor__colsample_bytree | 1 |
| xgbregressor__enable_categorical | False |
| xgbregressor__gamma | 0 |
| xgbregressor__gpu_id | -1 |
| xgbregressor__importance_type | |
| xgbregressor__interaction_constraints | |
| xgbregressor__learning_rate | 0.300000012 |
| xgbregressor__max_delta_step | 0 |
| xgbregressor__max_depth | 5 |
| xgbregressor__min_child_weight | 1 |
| xgbregressor__missing | nan |
| xgbregressor__monotone_constraints | () |
| xgbregressor__n_estimators | 100 |
| xgbregressor__n_jobs | 8 |
| xgbregressor__num_parallel_tree | 1 |
| xgbregressor__predictor | auto |
| xgbregressor__random_state | 0 |
| xgbregressor__reg_alpha | 0 |
| xgbregressor__reg_lambda | 1 |
| xgbregressor__scale_pos_weight | 1 |
| xgbregressor__subsample | 1 |
| xgbregressor__tree_method | exact |
| xgbregressor__validate_parameters | 1 |
| xgbregressor__verbosity |
Model Plot
The model plot is below.
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))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
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))])OneHotEncoder(handle_unknown='ignore', sparse=False)
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)
Evaluation Results
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How to Get Started with the Model
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Model Card Authors
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Citation
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