Function maxabs_scale

sklearn/preprocessing/_data.py:1458–1549

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. Parameter

(X, *, axis=0, copy=True)

    prefer_skip_nested_validation=False,
)
def 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} of shape (n_samples, n_features)
        The data.

    axis : {0, 1}, default=0
        Axis used to scale along. If 0, independently scale each feature,
        otherwise (if 1) scale each sample.

    copy : bool, default=True
        If False, try to avoid a copy and scale in place.
        This is not guaranteed to always work in place; e.g. if the data is
        a numpy array with an int dtype, a copy will be returned even with
        copy=False.

    Returns
    -------
    X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features)
        The transformed data.

    .. warning:: Risk of data leak

        Do not use :func:`~sklearn.preprocessing.maxabs_scale` unless you know
        what you are doing. A common mistake is to apply it to the entire data
        *before* splitting into training and test sets. This will bias the
        model evaluation because information would have leaked from the test
        set to the training set.
        In general, we recommend using
        :class:`~sklearn.preprocessing.MaxAbsScaler` within a
        :ref:`Pipeline <pipeline>` in order to prevent most risks of data
        leaking: `pipe = make_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