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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.


lassoLasso or elastic net regularization for linear models
ridgeRidge regression
lassoPlotTrace plot of lasso fit
fitrlinearFit linear regression model to high-dimensional data
predictPredict response of linear regression model
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots


RegressionLinearLinear regression model for high-dimensional data
RegressionPartitionedLinearCross-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.

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