Class RobustScaler

sklearn/preprocessing/_data.py:1552–1824

Scale features using statistics that are robust to outliers. This Scaler removes the median and scales the data according to the quantile range (defaults to IQR: Interquartile Range). The IQR is the range between the 1st quartile (25th quantile) and the 3rd quartile (75th quantile).

class RobustScaler(OneToOneFeatureMixin, TransformerMixin, BaseEstimator):
    """Scale features using statistics that are robust to outliers.

    This Scaler removes the median and scales the data according to
    the quantile range (defaults to IQR: Interquartile Range).
    The IQR is the range between the 1st quartile (25th quantile)
    and the 3rd quartile (75th quantile).

    Centering and scaling happen independently on each feature by
    computing the relevant statistics on the samples in the training
    set. Median and interquartile range are then stored to be used on
    later data using the :meth:`transform` method.

    Standardization of a dataset is a common preprocessing for many machine
    learning estimators. Typically this is done by removing the mean and
    scaling to unit variance. However, outliers can often influence the sample
    mean / variance in a negative way. In such cases, using the median and the
    interquartile range often give better results. For an example visualization
    and comparison to other scalers, refer to :ref:`Compare RobustScaler with
    other scalers <plot_all_scaling_robust_scaler_section>`.

    .. versionadded:: 0.17

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

    Parameters
    ----------
    with_centering : bool, default=True
        If `True`, center the data before scaling.
        This will cause :meth:`transform` to raise an exception when attempted
        on sparse matrices, because centering them entails building a dense
        matrix which in common use cases is likely to be too large to fit in
        memory.

    with_scaling : bool, default=True
        If `True`, scale the data to interquartile range.

    quantile_range : tuple (q_min, q_max), 0.0 < q_min < q_max < 100.0, \
        default=(25.0, 75.0)
        Quantile range used to calculate `scale_`. By default this is equal to
        the IQR, i.e., `q_min` is the first quantile and `q_max` is the third
        quantile.

        .. versionadded:: 0.18

    copy : bool, default=True
        If `False`, try to avoid a copy and do inplace scaling instead.
        This is not guaranteed to always work inplace; e.g. if the data is
        not a NumPy array or scipy.sparse CSR matrix, a copy may still be
        returned.

    unit_variance : bool, default=False
        If `True`, scale data so that normally distributed features have a
        variance of 1. In general, if the difference between the x-values of
        `q_max` and `q_min` for a standard normal distribution is greater
        than 1, the dataset will be scaled down. If less than 1, the dataset
        will be scaled up.