piml.models.GLMRegressor

class piml.models.GLMRegressor(feature_names=None, feature_types=None, l1_regularization=0, l2_regularization=0, fit_intercept=True, random_state=0)

A wrapper of generalized linear model regressor in scikit-learn.

Parameters:
feature_nameslist or None, default=None

The list of feature names.

feature_typeslist or None, default=None

The list of feature types. Available types include “numerical” and “categorical”.

l1_regularization: float, default=0

The strength of l1 regularization, l1_regularization must be non-negative float.

  • {l1_regularization=0 & l2_regularization=0}: Ordinary least squares Linear Regression.

  • {l1_regularization>0 & l2_regularization=0}: Lasso Regression.

  • {l1_regularization=0 & l2_regularization>0}: Ridge Regression.

  • {l1_regularization>0 & l2_regularization>0}: Elasticnet Regression.

l2_regularization: float, default=0

The strength of l2 regularization, l2_regularization must be non-negative float.

  • {l1_regularization=0 & l2_regularization=0}: Ordinary least squares Linear Regression.

  • {l1_regularization>0 & l2_regularization=0}: Lasso Regression.

  • {l1_regularization=0 & l2_regularization>0}: Ridge Regression.

  • {l1_regularization>0 & l2_regularization>0}: Elasticnet Regression.

fit_interceptbool, default=True

Whether to fit intercept.

random_stateint, default=0

Random state of model.

Attributes:
feature_names_list of str

The feature name list of all input features.

feature_types_list of str

The feature type list of all input features.

Methods

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

set_params
get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:
routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)

Get parameters for this estimator.

Parameters:
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:
paramsdict

Parameter names mapped to their values.

set_params(**parameters)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:
**paramsdict

Estimator parameters.

Returns:
selfestimator instance

Estimator instance.

Examples using piml.models.GLMRegressor

GLM Linear Regression (Bike Sharing)

GLM Linear Regression (Bike Sharing)