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"""Methods for scaling, centering, normalization, binarization, and more."""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
from sklearn.preprocessing._data import (
Binarizer,
KernelCenterer,
MaxAbsScaler,
MinMaxScaler,
Normalizer,
PowerTransformer,
QuantileTransformer,
RobustScaler,
StandardScaler,
add_dummy_feature,
binarize,
maxabs_scale,
minmax_scale,
normalize,
power_transform,
quantile_transform,
robust_scale,
scale,
)
from sklearn.preprocessing._discretization import KBinsDiscretizer
from sklearn.preprocessing._encoders import OneHotEncoder, OrdinalEncoder
from sklearn.preprocessing._function_transformer import FunctionTransformer
from sklearn.preprocessing._label import (
LabelBinarizer,
LabelEncoder,
MultiLabelBinarizer,
label_binarize,
)
from sklearn.preprocessing._polynomial import PolynomialFeatures, SplineTransformer
from sklearn.preprocessing._target_encoder import TargetEncoder
__all__ = [
"Binarizer",
"FunctionTransformer",
"KBinsDiscretizer",
"KernelCenterer",
"LabelBinarizer",
"LabelEncoder",
"MaxAbsScaler",
"MinMaxScaler",
"MultiLabelBinarizer",
"Normalizer",
"OneHotEncoder",
"OrdinalEncoder",
"PolynomialFeatures",
"PowerTransformer",
"QuantileTransformer",
"RobustScaler",
"SplineTransformer",
"StandardScaler",
"TargetEncoder",
"add_dummy_feature",
"binarize",
"label_binarize",
"maxabs_scale",
"minmax_scale",
"normalize",
"power_transform",
"quantile_transform",
"robust_scale",
"scale",
]
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