Model description
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Intended uses & limitations
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Training Procedure
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Hyperparameters
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| Hyperparameter | Value |
|---|---|
| objective | reg:squarederror |
| base_score | |
| booster | |
| callbacks | |
| colsample_bylevel | |
| colsample_bynode | |
| colsample_bytree | |
| device | |
| early_stopping_rounds | |
| enable_categorical | False |
| eval_metric | |
| feature_types | |
| gamma | |
| grow_policy | |
| importance_type | |
| interaction_constraints | |
| learning_rate | 0.01 |
| max_bin | |
| max_cat_threshold | |
| max_cat_to_onehot | |
| max_delta_step | |
| max_depth | |
| max_leaves | |
| min_child_weight | |
| missing | nan |
| monotone_constraints | |
| multi_strategy | |
| n_estimators | |
| n_jobs | |
| num_parallel_tree | |
| random_state | |
| reg_alpha | |
| reg_lambda | |
| sampling_method | |
| scale_pos_weight | |
| subsample | |
| tree_method | |
| validate_parameters | |
| verbosity |
Model Plot
XGBRegressor(base_score=None, booster=None, callbacks=None,colsample_bylevel=None, colsample_bynode=None,colsample_bytree=None, device=None, early_stopping_rounds=None,enable_categorical=False, eval_metric=None, feature_types=None,gamma=None, grow_policy=None, importance_type=None,interaction_constraints=None, learning_rate=0.01, max_bin=None,max_cat_threshold=None, max_cat_to_onehot=None,max_delta_step=None, max_depth=None, max_leaves=None,min_child_weight=None, missing=nan, monotone_constraints=None,multi_strategy=None, n_estimators=None, n_jobs=None,num_parallel_tree=None, random_state=None, ...)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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XGBRegressor(base_score=None, booster=None, callbacks=None,colsample_bylevel=None, colsample_bynode=None,colsample_bytree=None, device=None, early_stopping_rounds=None,enable_categorical=False, eval_metric=None, feature_types=None,gamma=None, grow_policy=None, importance_type=None,interaction_constraints=None, learning_rate=0.01, max_bin=None,max_cat_threshold=None, max_cat_to_onehot=None,max_delta_step=None, max_depth=None, max_leaves=None,min_child_weight=None, missing=nan, monotone_constraints=None,multi_strategy=None, n_estimators=None, n_jobs=None,num_parallel_tree=None, random_state=None, ...)
Evaluation Results
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