generalized_additive_models.Intercept#

class generalized_additive_models.Intercept#

An intercept term.

Examples

>>> intercept = Intercept()
>>> intercept.num_coefficients
1
>>> import numpy as np
>>> X = np.random.randn(5, 3)
>>> intercept.fit_transform(X)
array([[1.],
       [1.],
       [1.],
       [1.],
       [1.]])

Intercepts have no penalty:

>>> intercept.penalty_matrix()
array([[0.]])

Terms can yield themselves once, like so:

>>> list(intercept)
[Intercept()]

__init__()#: Initialize an Intercept.

Methods

`__init__`()	Initialize an Intercept.
`fit`(X)	Fit to data.
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`is_redundant_with_respect_to`(other)	Check if a Term is redundant with respect to another.
`penalty_matrix`()	Return the penalty matrix for the term.
`set_output`(*[, transform])	Set output container.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(X)	Transform the input.

Attributes

`by`	Multiplicative feature, not used in Intercept.
`feature`	Feature name, not used in Intercept.
`name`	Name of the term.
`num_coefficients`	Number of coefficients for the term.

by = None#: Multiplicative feature, not used in Intercept.

feature = None#: Feature name, not used in Intercept.

fit(X)#

Fit to data.

Parameters:: X (np.ndarray or pd.DataFrame) – A dataset of shape (num_samples, num_features).

name = 'intercept'#: Name of the term.

property num_coefficients#: Number of coefficients for the term.

penalty_matrix()#: Return the penalty matrix for the term. Intercepts have no penalty.

transform(X)#

Transform the input.

Parameters:: X (np.ndarray or pd.DataFrame) – A dataset of shape (num_samples, num_features).
Returns:: X – An ndarray for the term.
Return type:: np.ndarray

Examples

>>> intercept = Intercept()
>>> X = np.eye(3)
>>> intercept.fit_transform(X)
array([[1.],
       [1.],
       [1.]])