.. _`Polynomial Features`:

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

Polynomial Features
```````````````````

.. image:: polynomial.svg
   :width: 48


Generate polynomial and interaction features.




Documentation
:::::::::::::

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].

    Often it's useful to add complexity to a model by also considering nonlinear features of the
    input data. This can enhance the predictive power of the model.

Attributes
==========

    **n_input_features_**


    **n_output_features_**


    **powers_**


Definition
::::::::::


Output ports
============

    **model**
        | Type: model
        | Description: 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.







Examples
========





The node can be found in:

* :download:`Polynomial_Features.syx </Examples/Sympathy for Data/MachineLearning/Polynomial_Features.syx>`




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

.. automodule:: node_preprocessing
    :noindex:

.. class:: PolynomialFeatures
    :noindex: