smile.regression

Class ElasticNet

java.lang.Object

smile.regression.ElasticNet

public class ElasticNet
extends java.lang.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

Constructor Summary

Constructors

Constructor and Description
ElasticNet()

Method Summary

Modifier and Type	Method and Description
static LinearModel	fit(smile.data.formula.Formula formula, smile.data.DataFrame data, double lambda1, double lambda2) Fit an Elastic Net model.
static LinearModel	fit(smile.data.formula.Formula formula, smile.data.DataFrame data, double lambda1, double lambda2, double tol, int maxIter) Fit an Elastic Net model.
static LinearModel	fit(smile.data.formula.Formula formula, smile.data.DataFrame data, java.util.Properties prop) Fit an Elastic Net model.

Methods inherited from class java.lang.Object

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

Constructor Detail

ElasticNet

public ElasticNet()

Method Detail

fit

public static LinearModel fit(smile.data.formula.Formula formula,
                              smile.data.DataFrame data,
                              java.util.Properties prop)

Fit 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.

prop - Training algorithm hyper-parameters and properties.

fit

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

Fit an Elastic Net model. The hyper-parameters in prop include

• lambda1 is the shrinkage/regularization parameter for L1
• lambda2 is the shrinkage/regularization parameter for L2
• tolerance is the tolerance for stopping iterations (relative target duality gap).
• max.iterations is the maximum number of IPM (Newton) iterations.

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 shrinkage/regularization parameter for L1

lambda2 - the shrinkage/regularization parameter for L2

fit

public static LinearModel fit(smile.data.formula.Formula formula,
                              smile.data.DataFrame data,
                              double lambda1,
                              double lambda2,
                              double tol,
                              int maxIter)

Fit an Elastic Net model. The hyper-parameters in prop include

• lambda1 is the shrinkage/regularization parameter for L1
• lambda2 is the shrinkage/regularization parameter for L2
• tolerance is the tolerance for stopping iterations (relative target duality gap).
• max.iterations is the maximum number of IPM (Newton) iterations.

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 shrinkage/regularization parameter for L1

lambda2 - the shrinkage/regularization parameter for L2

tol - the tolerance for stopping iterations (relative target duality gap).

maxIter - the maximum number of IPM (Newton) iterations.