Class ElasticNet

java.lang.Object

smile.regression.ElasticNet

public class ElasticNet
extends Object

Elastic Net regularization. The elastic net is a regularized regression method that linearly combines the L1 and L2 penalties of the lasso and ridge methods.

The elastic net problem can be reduced to a lasso problem on modified data and response. And note that the penalty function of Elastic Net is strictly convex so there is a unique global minimum, even if input data matrix is not full rank.

References

1. Kevin P. Murphy: Machine Learning A Probabilistic Perspective, Section 13.5.3, 2012
2. Zou, Hui, Hastie, Trevor: Regularization and Variable Selection via the Elastic Net, 2005

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static final record	ElasticNet.Options	Elastic Net hyperparameters.

Method Summary

Modifier and Type	Method	Description
static LinearModel	fit(Formula formula, DataFrame data, double lambda1, double lambda2)	Fits an Elastic Net model.
static LinearModel	fit(Formula formula, DataFrame data, ElasticNet.Options options)	Fits an Elastic Net model.

Methods inherited from class Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Method Details

fit

public static LinearModel fit(Formula formula,
 DataFrame data,
 double lambda1,
 double lambda2)

Fits an Elastic Net model.

Parameters:

formula - a symbolic description of the model to be fitted.

data - the data frame of the explanatory and response variables. NO NEED to include a constant column of 1s for bias.

lambda1 - the L1 shrinkage/regularization parameter

lambda2 - the L2 shrinkage/regularization parameter

Returns:

the model.

fit

public static LinearModel fit(Formula formula,
 DataFrame data,
 ElasticNet.Options options)

Fits an Elastic Net model.

Parameters:

formula - a symbolic description of the model to be fitted.

data - the data frame of the explanatory and response variables. NO NEED to include a constant column of 1s for bias.

options - the hyperparameters.

Returns:

the model.