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Ensemble models. This module is styled after Scikit-Learn's ensemble module: https://scikit-learn.org/stable/modules/ensemble.html
Classes
RandomForestClassifier
RandomForestClassifier(
num_parallel_tree: int = 100,
tree_method: typing.Literal["auto", "exact", "approx", "hist"] = "auto",
min_tree_child_weight: int = 1,
colsample_bytree: float = 1.0,
colsample_bylevel: float = 1.0,
colsample_bynode: float = 0.8,
gamma: float = 0.0,
max_depth: int = 15,
subsample: float = 0.8,
reg_alpha: float = 0.0,
reg_lambda: float = 1.0,
early_stop=True,
min_rel_progress: float = 0.01,
enable_global_explain=False,
xgboost_version: typing.Literal["0.9", "1.1"] = "0.9",
)
A random forest classifier.
A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting.
RandomForestRegressor
RandomForestRegressor(
num_parallel_tree: int = 100,
tree_method: typing.Literal["auto", "exact", "approx", "hist"] = "auto",
min_tree_child_weight: int = 1,
colsample_bytree=1.0,
colsample_bylevel=1.0,
colsample_bynode=0.8,
gamma=0.0,
max_depth: int = 15,
subsample=0.8,
reg_alpha=0.0,
reg_lambda=1.0,
early_stop=True,
min_rel_progress=0.01,
enable_global_explain=False,
xgboost_version: typing.Literal["0.9", "1.1"] = "0.9",
)
A random forest regressor.
A random forest is a meta estimator that fits a number of classifying decision trees on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting.
XGBClassifier
XGBClassifier(
num_parallel_tree: int = 1,
booster: typing.Literal["gbtree", "dart"] = "gbtree",
dart_normalized_type: typing.Literal["tree", "forest"] = "tree",
tree_method: typing.Literal["auto", "exact", "approx", "hist"] = "auto",
min_tree_child_weight: int = 1,
colsample_bytree: float = 1.0,
colsample_bylevel: float = 1.0,
colsample_bynode: float = 1.0,
gamma: float = 0.0,
max_depth: int = 6,
subsample: float = 1.0,
reg_alpha: float = 0.0,
reg_lambda: float = 1.0,
early_stop: bool = True,
learning_rate: float = 0.3,
max_iterations: int = 20,
min_rel_progress: float = 0.01,
enable_global_explain: bool = False,
xgboost_version: typing.Literal["0.9", "1.1"] = "0.9",
)
XGBoost classifier model.
Parameters | |
---|---|
Name | Description |
num_parallel_tree |
Optional[int]
Number of parallel trees constructed during each iteration. Default to 1. |
booster |
Optional[str]
Specify which booster to use: gbtree or dart. Default to "gbtree". |
dart_normalized_type |
Optional[str]
Type of normalization algorithm for DART booster. Possible values: "TREE", "FOREST". Default to "TREE". |
tree_method |
Optional[str]
Specify which tree method to use. Default to "auto". If this parameter is set to default, XGBoost will choose the most conservative option available. Possible values: ""exact", "approx", "hist". |
min_child_weight |
Optional[float]
Minimum sum of instance weight(hessian) needed in a child. Default to 1. |
colsample_bytree |
Optional[float]
Subsample ratio of columns when constructing each tree. Default to 1.0. |
colsample_bylevel |
Optional[float]
Subsample ratio of columns for each level. Default to 1.0. |
colsample_bynode |
Optional[float]
Subsample ratio of columns for each split. Default to 1.0. |
gamma |
Optional[float]
(min_split_loss) Minimum loss reduction required to make a further partition on a leaf node of the tree. Default to 0.0. |
max_depth |
Optional[int]
Maximum tree depth for base learners. Default to 6. |
subsample |
Optional[float]
Subsample ratio of the training instance. Default to 1.0. |
reg_alpha |
Optional[float]
L1 regularization term on weights (xgb's alpha). Default to 0.0. |
reg_lambda |
Optional[float]
L2 regularization term on weights (xgb's lambda). Default to 1.0. |
early_stop |
Optional[bool]
Whether training should stop after the first iteration. Default to True. |
learning_rate |
Optional[float]
Boosting learning rate (xgb's "eta"). Default to 0.3. |
max_iterations |
Optional[int]
Maximum number of rounds for boosting. Default to 20. |
min_rel_progress |
Optional[float]
Minimum relative loss improvement necessary to continue training when early_stop is set to True. Default to 0.01. |
enable_global_explain |
Optional[bool]
Whether to compute global explanations using explainable AI to evaluate global feature importance to the model. Default to False. |
xgboost_version |
Optional[str]
Specifies the Xgboost version for model training. Default to "0.9". Possible values: "0.9", "1.1". |
XGBRegressor
XGBRegressor(
num_parallel_tree: int = 1,
booster: typing.Literal["gbtree", "dart"] = "gbtree",
dart_normalized_type: typing.Literal["tree", "forest"] = "tree",
tree_method: typing.Literal["auto", "exact", "approx", "hist"] = "auto",
min_tree_child_weight: int = 1,
colsample_bytree: float = 1.0,
colsample_bylevel: float = 1.0,
colsample_bynode: float = 1.0,
gamma: float = 0.0,
max_depth: int = 6,
subsample: float = 1.0,
reg_alpha: float = 0.0,
reg_lambda: float = 1.0,
early_stop: float = True,
learning_rate: float = 0.3,
max_iterations: int = 20,
min_rel_progress: float = 0.01,
enable_global_explain: bool = False,
xgboost_version: typing.Literal["0.9", "1.1"] = "0.9",
)
XGBoost regression model.
Parameters | |
---|---|
Name | Description |
num_parallel_tree |
Optional[int]
Number of parallel trees constructed during each iteration. Default to 1. |
booster |
Optional[str]
Specify which booster to use: gbtree or dart. Default to "gbtree". |
dart_normalized_type |
Optional[str]
Type of normalization algorithm for DART booster. Possible values: "TREE", "FOREST". Default to "TREE". |
tree_method |
Optional[str]
Specify which tree method to use. Default to "auto". If this parameter is set to default, XGBoost will choose the most conservative option available. Possible values: ""exact", "approx", "hist". |
min_child_weight |
Optional[float]
Minimum sum of instance weight(hessian) needed in a child. Default to 1. |
colsample_bytree |
Optional[float]
Subsample ratio of columns when constructing each tree. Default to 1.0. |
colsample_bylevel |
Optional[float]
Subsample ratio of columns for each level. Default to 1.0. |
colsample_bynode |
Optional[float]
Subsample ratio of columns for each split. Default to 1.0. |
gamma |
Optional[float]
(min_split_loss) Minimum loss reduction required to make a further partition on a leaf node of the tree. Default to 0.0. |
max_depth |
Optional[int]
Maximum tree depth for base learners. Default to 6. |
subsample |
Optional[float]
Subsample ratio of the training instance. Default to 1.0. |
reg_alpha |
Optional[float]
L1 regularization term on weights (xgb's alpha). Default to 0.0. |
reg_lambda |
Optional[float]
L2 regularization term on weights (xgb's lambda). Default to 1.0. |
early_stop |
Optional[bool]
Whether training should stop after the first iteration. Default to True. |
learning_rate |
Optional[float]
Boosting learning rate (xgb's "eta"). Default to 0.3. |
max_iterations |
Optional[int]
Maximum number of rounds for boosting. Default to 20. |
min_rel_progress |
Optional[float]
Minimum relative loss improvement necessary to continue training when early_stop is set to True. Default to 0.01. |
enable_global_explain |
Optional[bool]
Whether to compute global explanations using explainable AI to evaluate global feature importance to the model. Default to False. |
xgboost_version |
Optional[str]
Specifies the Xgboost version for model training. Default to "0.9". Possible values: "0.9", "1.1". |