sklearn.preprocessing#

Methods for scaling, centering, normalization, binarization, and more.

User guide. See the Preprocessing data section for further details.

`Binarizer`	Binarize data (set feature values to 0 or 1) according to a threshold.
`FunctionTransformer`	Constructs a transformer from an arbitrary callable.
`KBinsDiscretizer`	Bin continuous data into intervals.
`KernelCenterer`	Center an arbitrary kernel matrix \(K\).
`LabelBinarizer`	Binarize labels in a one-vs-all fashion.
`LabelEncoder`	Encode target labels with value between 0 and n_classes-1.
`MaxAbsScaler`	Scale each feature by its maximum absolute value.
`MinMaxScaler`	Transform features by scaling each feature to a given range.
`MultiLabelBinarizer`	Transform between iterable of iterables and a multilabel format.
`Normalizer`	Normalize samples individually to unit norm.
`OneHotEncoder`	Encode categorical features as a one-hot numeric array.
`OrdinalEncoder`	Encode categorical features as an integer array.
`PolynomialFeatures`	Generate polynomial and interaction features.
`PowerTransformer`	Apply a power transform featurewise to make data more Gaussian-like.
`QuantileTransformer`	Transform features using quantiles information.
`RobustScaler`	Scale features using statistics that are robust to outliers.
`SplineTransformer`	Generate univariate B-spline bases for features.
`StandardScaler`	Standardize features by removing the mean and scaling to unit variance.
`TargetEncoder`	Target Encoder for regression and classification targets.
`add_dummy_feature`	Augment dataset with an additional dummy feature.
`binarize`	Boolean thresholding of array-like or scipy.sparse matrix.
`label_binarize`	Binarize labels in a one-vs-all fashion.
`maxabs_scale`	Scale each feature to the [-1, 1] range without breaking the sparsity.
`minmax_scale`	Transform features by scaling each feature to a given range.
`normalize`	Scale input vectors individually to unit norm (vector length).
`power_transform`	Parametric, monotonic transformation to make data more Gaussian-like.
`quantile_transform`	Transform features using quantiles information.
`robust_scale`	Standardize a dataset along any axis.
`scale`	Standardize a dataset along any axis.