Regularization
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 |
Classes
RegressionLinear | Linear regression model for high-dimensional data |
RegressionPartitionedLinear | Cross-validated linear regression model for high-dimensional data |
Topics
- 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.