Polynomial Features

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:

• Polynomial_Features.syx

Implementation

class node_preprocessing.PolynomialFeatures