.. _`Polynomial Features`:

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

Polynomial Features
~~~~~~~~~~~~~~~~~~~

.. 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**

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

*Configuration*:


  - *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`.


  - *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.


  - *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).




*Attributes*:


  - *n_input_features_*



  - *n_output_features_*



  - *powers_*





*Input ports*:


*Output ports*:
    **model** : model
        Model


**Definition**


*Input ports*


*Output ports*

    :model:  model

        Model





.. automodule:: node_preprocessing
    :noindex:

.. class:: PolynomialFeatures
    :noindex: