Class MinMaxScaler

sklearn/preprocessing/_data.py:305–621

Transform features by scaling each feature to a given range. This estimator scales and translates each feature individually such that it is in the given range on the training set, e.g. between zero and one. The transformation is given by:: X_std = (X - X.min(axis=0)) / (X.

class MinMaxScaler(OneToOneFeatureMixin, TransformerMixin, BaseEstimator):
    """Transform features by scaling each feature to a given range.

    This estimator scales and translates each feature individually such
    that it is in the given range on the training set, e.g. between
    zero and one.

    The transformation is given by::

        X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))
        X_scaled = X_std * (max - min) + min

    where min, max = feature_range.

    This transformation is often used as an alternative to zero mean,
    unit variance scaling.

    `MinMaxScaler` doesn't reduce the effect of outliers, but it linearly
    scales them down into a fixed range, where the largest occurring data point
    corresponds to the maximum value and the smallest one corresponds to the
    minimum value. For an example visualization, refer to :ref:`Compare
    MinMaxScaler with other scalers <plot_all_scaling_minmax_scaler_section>`.

    Read more in the :ref:`User Guide <preprocessing_scaler>`.

    Parameters
    ----------
    feature_range : tuple (min, max), default=(0, 1)
        Desired range of transformed data.

    copy : bool, default=True
        Set to False to perform inplace row normalization 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
        provided `feature_range`.
        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 `feature_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.

        .. versionadded:: 0.24

    Attributes
    ----------
    min_ : ndarray of shape (n_features,)
        Per feature adjustment for minimum. Equivalent to
        ``min - X.min(axis=0) * self.scale_``

    scale_ : ndarray of shape (n_features,)
        Per feature relative scaling of the data. Equivalent to
        ``(max - min) / (X.max(axis=0) - X.min(axis=0))``