Class MaxAbsScaler

sklearn/preprocessing/_data.py:1190–1448

Scale each feature by its maximum absolute value. This estimator scales and translates each feature individually such that the maximal absolute value of each feature in the training set will be 1.0. It does not shift/center the data, and thus does not destroy any sparsity. This

class MaxAbsScaler(OneToOneFeatureMixin, TransformerMixin, BaseEstimator):
    """Scale each feature by its maximum absolute value.

    This estimator scales and translates each feature individually such
    that the maximal absolute value of each feature in the
    training set will be 1.0. It does not shift/center the data, and
    thus does not destroy any sparsity.

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

    `MaxAbsScaler` doesn't reduce the effect of outliers; it only linearly
    scales them down. For an example visualization, refer to :ref:`Compare
    MaxAbsScaler with other scalers <plot_all_scaling_max_abs_scaler_section>`.

    .. versionadded:: 0.17

    Parameters
    ----------
    copy : bool, default=True
        Set to False to perform inplace scaling and avoid a copy (if the input
        is already a numpy array).

    clip : bool, default=False
        Set to True to clip transformed values of held-out data to [-1, 1].
        Since this parameter will clip values, `inverse_transform` may not
        be able to restore the original data.

        .. note::
            Setting `clip=True` does not prevent feature drift (a distribution
            shift between training and test data). The transformed values are clipped
            to the [-1, 1] range, which helps avoid unintended behavior in models
            sensitive to out-of-range inputs (e.g. linear models). Use with care,
            as clipping can distort the distribution of test data.

    Attributes
    ----------
    scale_ : ndarray of shape (n_features,)
        Per feature relative scaling of the data.

        .. versionadded:: 0.17
           *scale_* attribute.

    max_abs_ : ndarray of shape (n_features,)
        Per feature maximum absolute value.

    n_features_in_ : int
        Number of features seen during :term:`fit`.

        .. versionadded:: 0.24

    feature_names_in_ : ndarray of shape (`n_features_in_`,)
        Names of features seen during :term:`fit`. Defined only when `X`
        has feature names that are all strings.

        .. versionadded:: 1.0

    n_samples_seen_ : int
        The number of samples processed by the estimator. Will be reset on