.. _`Polynomial Features`:

.. _`org.sysess.sympathy.machinelearning.polynomial_features`:

Polynomial Features
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.. image:: polynomial.svg
   :width: 48


Generate a new feature matrix consisting of all polynomial combinations of the features with less than a given degree




Documentation
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Attributes
==========

    **n_input_features_**


    **n_output_features_**


    **powers_**


Definition
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Output ports
============

    **model**  model
        Model




Configuration
=============

    **Degree** (degree)
        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`.
    **Include bias** (include_bias)
        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).
    **Only interaction features produced** (interaction_only)
        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`, `x`, `x * x`, etc.
            - excluded: `x ** 2`, `x ** 2 * x`, etc.









Implementation
==============

.. automodule:: node_preprocessing
    :noindex:

.. class:: PolynomialFeatures
    :noindex: