pipeline(MaxAbsScaler(), LogisticRegression())`.

See Also
--------
MaxAbsScaler : Performs scaling to the [-1, 1] range using
    the Transformer API (e.g. as part of a preprocessing
    :class:`~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: :ref:`sphx_glr_auto_examples_preprocessing_plot_all_scaling.py`.

Examples
--------
>>> from sklearn.preprocessing import maxabs_scale
>>> X = [[-2, 1, 2], [-1, 0, 1]]
>>> maxabs_scale(X, axis=0)  # scale each column independently
array([[-1. ,  1. ,  1. ],
       [-0.5,  0. ,  0.5]])
>>> maxabs_scale(X, axis=1)  # scale each row independently
array([[-1. ,  0.5,  1. ],
       [-1. ,  0. ,  1. ]])
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