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"""
The :mod:`sklearn.ensemble` module includes ensemble-based methods for
classification, regression and anomaly detection.
"""
import typing
from ._base import BaseEnsemble
from ._forest import RandomForestClassifier
from ._forest import RandomForestRegressor
from ._forest import RandomTreesEmbedding
from ._forest import ExtraTreesClassifier
from ._forest import ExtraTreesRegressor
from ._bagging import BaggingClassifier
from ._bagging import BaggingRegressor
from ._iforest import IsolationForest
from ._weight_boosting import AdaBoostClassifier
from ._weight_boosting import AdaBoostRegressor
from ._gb import GradientBoostingClassifier
from ._gb import GradientBoostingRegressor
from ._voting import VotingClassifier
from ._voting import VotingRegressor
from ._stacking import StackingClassifier
from ._stacking import StackingRegressor
if typing.TYPE_CHECKING:
# Avoid errors in type checkers (e.g. mypy) for experimental estimators.
# TODO: remove this check once the estimator is no longer experimental.
from ._hist_gradient_boosting.gradient_boosting import ( # noqa
HistGradientBoostingRegressor, HistGradientBoostingClassifier
)
__all__ = ["BaseEnsemble",
"RandomForestClassifier", "RandomForestRegressor",
"RandomTreesEmbedding", "ExtraTreesClassifier",
"ExtraTreesRegressor", "BaggingClassifier",
"BaggingRegressor", "IsolationForest", "GradientBoostingClassifier",
"GradientBoostingRegressor", "AdaBoostClassifier",
"AdaBoostRegressor", "VotingClassifier", "VotingRegressor",
"StackingClassifier", "StackingRegressor",
]
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