Documentation

This is machine translation

Translated by Microsoft
Mouse over text to see original. Click the button below to return to the English verison of the page.

Regularization

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.

Classes

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

Functions

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.

Concepts

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.

Was this topic helpful?