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Ridge regression, lasso, elastic nets

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 that fit in the MATLAB® Workspace, fit a regularized linear regression model using fitrlinear.


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


lasso Regularized least-squares regression using lasso or elastic net algorithms
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

Examples and How To

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.


Introduction to Ridge Regression

Ridge regression addresses the problem of multicollinearity (correlated model terms) in linear regression problems.

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.

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