Class StandardScaler

sklearn/preprocessing/_data.py:742–1187

Standardize features by removing the mean and scaling to unit variance. The standard score of a sample `x` is calculated as: .. code-block:: text z = (x - u) / s where `u` is the mean of the training samples or zero if `with_mean=False`, and `s` is the standard deviation

class StandardScaler(
    CallbackSupportMixin, OneToOneFeatureMixin, TransformerMixin, BaseEstimator
):
    """Standardize features by removing the mean and scaling to unit variance.

    The standard score of a sample `x` is calculated as:

    .. code-block:: text

        z = (x - u) / s

    where `u` is the mean of the training samples or zero if `with_mean=False`,
    and `s` is the standard deviation of the training samples or one if
    `with_std=False`.

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

    Standardization of a dataset is a common requirement for many
    machine learning estimators: they might behave badly if the
    individual features do not more or less look like standard normally
    distributed data (e.g. Gaussian with 0 mean and unit variance).

    For instance many elements used in the objective function of
    a learning algorithm (such as the RBF kernel of Support Vector
    Machines or the L1 and L2 regularizers of linear models) assume that
    all features are centered around 0 and have variance in the same
    order. If a feature has a variance that is orders of magnitude larger
    than others, it might dominate the objective function and make the
    estimator unable to learn from other features correctly as expected.

    `StandardScaler` is sensitive to outliers, and the features may scale
    differently from each other in the presence of outliers. For an example
    visualization, refer to :ref:`Compare StandardScaler with other scalers
    <plot_all_scaling_standard_scaler_section>`.

    This scaler can also be applied to sparse CSR or CSC matrices by passing
    `with_mean=False` to avoid breaking the sparsity structure of the data.

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

    Parameters
    ----------
    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.

    with_mean : bool, default=True
        If True, center the data before scaling.
        This does not work (and will 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.