Class QuantileTransformer

sklearn/preprocessing/_data.py:2670–3106

Transform features using quantiles information. This method transforms the features to follow a uniform or a normal distribution. Therefore, for a given feature, this transformation tends to spread out the most frequent values. It also reduces the impact of (marginal) outliers: this

class QuantileTransformer(OneToOneFeatureMixin, TransformerMixin, BaseEstimator):
    """Transform features using quantiles information.

    This method transforms the features to follow a uniform or a normal
    distribution. Therefore, for a given feature, this transformation tends
    to spread out the most frequent values. It also reduces the impact of
    (marginal) outliers: this is therefore a robust preprocessing scheme.

    The transformation is applied on each feature independently. First an
    estimate of the cumulative distribution function of a feature is
    used to map the original values to a uniform distribution. The obtained
    values are then mapped to the desired output distribution using the
    associated quantile function. Features values of new/unseen data that fall
    below or above the fitted range will be mapped to the bounds of the output
    distribution. Note that this transform is non-linear. It may distort linear
    correlations between variables measured at the same scale but renders
    variables measured at different scales more directly comparable.

    For example visualizations, refer to :ref:`Compare QuantileTransformer with
    other scalers <plot_all_scaling_quantile_transformer_section>`.

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

    .. versionadded:: 0.19

    Parameters
    ----------
    n_quantiles : int, default=1000 or n_samples
        Number of quantiles to be computed. It corresponds to the number
        of landmarks used to discretize the cumulative distribution function.
        If n_quantiles is larger than the number of samples, n_quantiles is set
        to the number of samples as a larger number of quantiles does not give
        a better approximation of the cumulative distribution function
        estimator.

    output_distribution : {'uniform', 'normal'}, default='uniform'
        Marginal distribution for the transformed data. The choices are
        'uniform' (default) or 'normal'.

    ignore_implicit_zeros : bool, default=False
        Only applies to sparse matrices. If True, the sparse entries of the
        matrix are discarded to compute the quantile statistics. If False,
        these entries are treated as zeros.

    subsample : int or None, default=10_000
        Maximum number of samples used to estimate the quantiles for
        computational efficiency. Note that the subsampling procedure may
        differ for value-identical sparse and dense matrices.
        Disable subsampling by setting `subsample=None`.

        .. versionadded:: 1.5
           The option `None` to disable subsampling was added.

    random_state : int, RandomState instance or None, default=None
        Determines random number generation for subsampling and smoothing
        noise.
        Please see ``subsample`` for more details.
        Pass an int for reproducible results across multiple function calls.