Generalized Regression Models
Build Models Using Regularization Techniques
In JMP Pro, the Fit Model platform’s Generalized Regression personality provides shrinkage techniques that specifically address modeling correlated and high-dimensional data. Two of these techniques, the Lasso and the Elastic Net, perform variable selection as part of the modeling procedure.
Large data sets that contain many variables typically evidence multicollinearity issues. Modern data sets often include more variables than observations, requiring variable selection if traditional modeling techniques are to be used. The presence of multicollinearity and ...