For greater accuracy on low- through medium-dimensional data
sets, implement least-squares regression with regularization using
For reduced computation time on high-dimensional data sets that
fit in the MATLAB® Workspace, fit a regularized linear regression
|Regularized least-squares regression using lasso or elastic net algorithms|
|Trace plot of lasso fit|
|Fit linear regression model to high-dimensional data|
|Predict response of linear regression model|
|Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots|
lasso identifies and
discards unnecessary predictors.
Predict the mileage (MPG) of a car based on its weight,
displacement, horsepower, and acceleration using
Identify important predictors using
Ridge regression addresses the problem of multicollinearity (correlated model terms) in linear regression problems.
lasso algorithm is a regularization
technique and shrinkage estimator. The related elastic net algorithm
is more suitable when predictors are highly correlated.