Regularization

Ridge regression, lasso, and elastic nets for linear models

For greater accuracy on low- through medium-dimensional data sets, implement least-squares regression with regularization using lasso or ridge.

For reduced computation time on high-dimensional data sets, fit a regularized linear regression model using fitrlinear.

Functions

`lasso`	Lasso or elastic net regularization for linear models
`ridge`	Ridge regression
`lassoPlot`	Trace plot of lasso fit

`fitrlinear`	Fit linear regression model to high-dimensional data
`predict`	Predict response of linear regression model

`RegressionLinear`	Linear regression model for high-dimensional data
`RegressionPartitionedLinear`	Cross-validated linear regression model for high-dimensional data

Lasso Regularization
See how lasso identifies and discards unnecessary predictors.
Lasso and Elastic Net with Cross Validation
Predict the mileage (MPG) of a car based on its weight, displacement, horsepower, and acceleration using lasso and elastic net.
Wide Data via Lasso and Parallel Computing
Identify important predictors using lasso and cross-validation.
Lasso and Elastic Net
The lasso algorithm is a regularization technique and shrinkage estimator. The related elastic net algorithm is more suitable when predictors are highly correlated.
Ridge Regression
Ridge regression addresses the problem of multicollinearity (correlated model terms) in linear regression problems.