sklearn.preprocessing.maxabs_scale

sklearn.preprocessing.maxabs_scale(X, axis=0, copy=True) [source]

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, shape (n_samples, n_features) The data. axis : int (0 by default) axis used to scale along. If 0, independently scale each feature, otherwise (if 1) scale each sample. copy : boolean, optional, default is True Set to False to perform inplace scaling and avoid a copy (if the input is already a numpy array).

See also

MaxAbsScaler

Performs scaling to the [-1, 1] range using the``Transformer`` API (e.g. as part of a preprocessing sklearn.pipeline.Pipeline).

Notes

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

For a comparison of the different scalers, transformers, and normalizers, see examples/preprocessing/plot_all_scaling.py.