Methods for scaling, centering, normalization, binarization, and more.
Binarize data (set feature values to 0 or 1) according to a threshold.
Constructs a transformer from an arbitrary callable.
Bin continuous data into intervals.
Center an arbitrary kernel matrix \(K\).
Binarize labels in a one-vs-all fashion.
Encode target labels with value between 0 and n_classes-1.
Scale each feature by its maximum absolute value.
Transform features by scaling each feature to a given range.
Transform between iterable of iterables and a multilabel format.
Normalize samples individually to unit norm.
Encode categorical features as a one-hot numeric array.
Encode categorical features as an integer array.
Generate polynomial and interaction features.
Apply a power transform featurewise to make data more Gaussian-like.
Transform features using quantiles information.
Scale features using statistics that are robust to outliers.
Generate univariate B-spline bases for features.
Standardize features by removing the mean and scaling to unit variance.
Target Encoder for regression and classification targets.
Augment dataset with an additional dummy feature.
Boolean thresholding of array-like or scipy.sparse matrix.
Binarize labels in a one-vs-all fashion.
Scale each feature to the [-1, 1] range without breaking the sparsity.
Transform features by scaling each feature to a given range.
Scale input vectors individually to unit norm (vector length).
Parametric, monotonic transformation to make data more Gaussian-like.
Transform features using quantiles information.
Standardize a dataset along any axis.
Standardize a dataset along any axis.
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