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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
(
n_estimators
:
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
,
tol
:
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.
n_estimators
Optional[int]
Number of parallel trees constructed during each iteration. Default to 100. Minimum value is 2.
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. The value should be between 0 and 1.
colsample_bylevel
Optional[float]
Subsample ratio of columns for each level. Default to 1.0. The value should be between 0 and 1.
colsample_bynode
Optional[float]
Subsample ratio of columns for each split. Default to 0.8. The value should be between 0 and 1.
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 15. The value should be greater than 0 and less than 1.
subsample
Optional[float]
Subsample ratio of the training instance. Default to 0.8. The value should be greater than 0 and less than 1.
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.
tol
Optional[float]
Minimum relative loss improvement necessary to continue training. 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".ß
RandomForestRegressor
RandomForestRegressor
(
n_estimators
:
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
,
tol
=
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.
n_estimators
Optional[int]
Number of parallel trees constructed during each iteration. Default to 100. Minimum value is 2.
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. The value should be between 0 and 1.
colsample_bylevel
Optional[float]
Subsample ratio of columns for each level. Default to 1.0. The value should be between 0 and 1.
colsample_bynode
Optional[float]
Subsample ratio of columns for each split. Default to 0.8. The value should be between 0 and 1.
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 15. The value should be greater than 0 and less than 1.
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.
tol
Optional[float]
Minimum relative loss improvement necessary to continue training. 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".
XGBClassifier
XGBClassifier
(
n_estimators
:
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
,
learning_rate
:
float
=
0.3
,
max_iterations
:
int
=
20
,
tol
:
float
=
0.01
,
enable_global_explain
:
bool
=
False
,
xgboost_version
:
typing
.
Literal
[
"0.9"
,
"1.1"
]
=
"0.9"
)
XGBoost classifier model.
n_estimators
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.
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.
tol
Optional[float]
Minimum relative loss improvement necessary to continue training. 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
(
n_estimators
:
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
,
learning_rate
:
float
=
0.3
,
max_iterations
:
int
=
20
,
tol
:
float
=
0.01
,
enable_global_explain
:
bool
=
False
,
xgboost_version
:
typing
.
Literal
[
"0.9"
,
"1.1"
]
=
"0.9"
)
XGBoost regression model.
n_estimators
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.
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.
tol
Optional[float]
Minimum relative loss improvement necessary to continue training. 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".
