generalized_additive_models.Logit#

class generalized_additive_models.Logit(low=0, high=1)#

Logit link: \(g(\mu) = \log(\mu / (1 - \mu))\)

Methods

`__init__`([low, high])
`derivative`(mu)	Elementwise first derivative of the link function.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`inverse_link`(linear_prediction)	Map from the linear space to the expected value \(\mu\).
`link`(mu)	Map from the expected value \(\mu\) to the unbounded linear space.
`second_derivative`(mu)	Elementwise second derivative of the link function.
`set_params`(**params)	Set the parameters of this estimator.

Attributes

`domain`	Domain of the link function
`name`	Name of the link function

inverse_link(linear_prediction)#: Map from the linear space to the expected value \(\mu\).

link(mu)#

Map from the expected value \(\mu\) to the unbounded linear space.

Examples

>>> mu = np.array([0.1, 0.5, 0.9, 0.99, 0.999])
>>> Logit().link(mu)
array([-2.19722458,  0.        ,  2.19722458,  4.59511985,  6.90675478])