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, fit a
regularized linear regression model using
Regularization Without Using Object
|Fit linear regression model to high-dimensional data|
|Predict response of linear regression model|
|Linear regression model for high-dimensional data|
|Cross-validated linear regression model for high-dimensional data|
- Lasso Regularization
lassoidentifies 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
lassoand elastic net.
- Wide Data via Lasso and Parallel Computing
Identify important predictors using
- Lasso and Elastic Net
lassoalgorithm 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.