GridSearchCV(cv=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=5, test_size=14),
estimator=Pipeline(steps=[('tree_feature_engineering',
Pipeline(steps=[('tree_preprocessor',
ColumnTransformer(remainder='passthrough',
transformers=[('cat',
Pipeline(steps=[('one_hot',
OneHotEncoder())]),
['season',
'mnth',
'holiday',
'weekday',
'workingday',
'weathersit'])]))...
n_estimators=100,
n_jobs=None,
num_parallel_tree=None,
predictor=None,
random_state=None,
reg_alpha=None,
reg_lambda=None,
scale_pos_weight=None,
subsample=None,
tree_method=None,
validate_parameters=None,
verbosity=None))]),
param_grid={'tree_regressor__max_depth': [3, 5, 8, 13],
'tree_regressor__min_child_weight': [0.01, 0.5, 1,
10]},
scoring='neg_root_mean_squared_error')Pipeline(steps=[('tree_preprocessor',
ColumnTransformer(remainder='passthrough',
transformers=[('cat',
Pipeline(steps=[('one_hot',
OneHotEncoder())]),
['season', 'mnth', 'holiday',
'weekday', 'workingday',
'weathersit'])]))])ColumnTransformer(remainder='passthrough',
transformers=[('cat',
Pipeline(steps=[('one_hot', OneHotEncoder())]),
['season', 'mnth', 'holiday', 'weekday',
'workingday', 'weathersit'])])['season', 'mnth', 'holiday', 'weekday', 'workingday', 'weathersit']
OneHotEncoder()
passthrough
XGBRegressor(base_score=None, booster=None, colsample_bylevel=None,
colsample_bynode=None, colsample_bytree=None,
enable_categorical=False, gamma=None, gpu_id=None,
importance_type=None, interaction_constraints=None,
learning_rate=None, max_delta_step=None, max_depth=None,
min_child_weight=None, missing=nan, monotone_constraints=None,
n_estimators=100, n_jobs=None, num_parallel_tree=None,
predictor=None, random_state=None, reg_alpha=None, reg_lambda=None,
scale_pos_weight=None, subsample=None, tree_method=None,
validate_parameters=None, verbosity=None)