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))])

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Evaluation Results

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How to Get Started with the Model

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