Class PolynomialFeatures

sklearn/preprocessing/_polynomial.py:92–583

Generate polynomial and interaction features. Generate a new feature matrix consisting of all polynomial combinations of the features with degree less than or equal to the specified degree. For example, if an input sample is two dimensional and of the form [a, b], the degree-2 polyn

class PolynomialFeatures(TransformerMixin, BaseEstimator):
    """Generate polynomial and interaction features.

    Generate a new feature matrix consisting of all polynomial combinations
    of the features with degree less than or equal to the specified degree.
    For example, if an input sample is two dimensional and of the form
    [a, b], the degree-2 polynomial features are [1, a, b, a^2, ab, b^2].

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

    Parameters
    ----------
    degree : int or tuple (min_degree, max_degree), default=2
        If a single int is given, it specifies the maximal degree of the
        polynomial features. If a tuple `(min_degree, max_degree)` is passed,
        then `min_degree` is the minimum and `max_degree` is the maximum
        polynomial degree of the generated features. Note that `min_degree=0`
        and `min_degree=1` are equivalent as outputting the degree zero term is
        determined by `include_bias`.

    interaction_only : bool, default=False
        If `True`, only interaction features are produced: features that are
        products of at most `degree` *distinct* input features, i.e. terms with
        power of 2 or higher of the same input feature are excluded:

        - included: `x[0]`, `x[1]`, `x[0] * x[1]`, etc.
        - excluded: `x[0] ** 2`, `x[0] ** 2 * x[1]`, etc.

    include_bias : bool, default=True
        If `True` (default), then include a bias column, the feature in which
        all polynomial powers are zero (i.e. a column of ones - acts as an
        intercept term in a linear model).

    order : {'C', 'F'}, default='C'
        Order of output array in the dense case. `'F'` order is faster to
        compute, but may slow down subsequent estimators.

        .. versionadded:: 0.21

    Attributes
    ----------
    powers_ : ndarray of shape (`n_output_features_`, `n_features_in_`)
        `powers_[i, j]` is the exponent of the jth input in the ith output.

    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_output_features_ : int
        The total number of polynomial output features. The number of output
        features is computed by iterating over all suitably sized combinations