maxabs_scale

cuml.preprocessing.maxabs_scale(X, *, axis=0, copy=True)

Scale each feature to the [-1, 1] range without breaking the sparsity.

This estimator scales each feature individually such that the maximal absolute value of each feature in the training set will be 1.0.

This scaler can also be applied to sparse CSR or CSC matrices.

Parameters:

X{array-like, sparse matrix}, shape (n_samples, n_features)

The data.

axisint (0 by default)

axis used to scale along. If 0, independently scale each feature, otherwise (if 1) scale each sample.

copyboolean, optional, default is True

Whether a forced copy will be triggered. If copy=False, a copy might be triggered by a conversion.

See also

MaxAbsScaler

Performs scaling to the [-1, 1] range using the``Transformer`` API

Notes

NaNs are treated as missing values: disregarded to compute the statistics, and maintained during the data transformation.