Fit Automatically Selected by Training
Selects the best available Fit from a list of available methods you have chosen. In addition to selecting the best method, Fit Automatically Selected by Training also automatically adjusts the individual settings (often called hyperparameters) to find the optimizing, predictive performance while avoiding overfitting.
Usability Characteristics
 Fits both noisy and nonnoisy data.
 Reduces the methods on which Fit Automatically Selected by Training iterates in order to reduce the run time used to build the Fit.
 Can run in multiexecute, while simultaneously iterating over multiple responses.
 The Stepwise Regression Terms option for Least Squares Regression reduces the number of coefficients in the regression model to contain only the set that is statistically significant.
 The behavior and characteristics of the underlying methods are the same as when the methods are directly applied. See their respective documentation pages for details.
 Gradient information can be used to boost performance for the methods that support gradients.
Settings
Parameter  Default  Range  Description 

Least Square Regression  On  On or Off 

Stepwise Regression Terms  Full Quadratic 

Controls the maximal set of terms considered in stepwise
Least Squares Regression.

Moving Least Squares  On  On or Off 

Radial Basis Function  On  On or Off 

Use Gradient Data  On  On or Off 
