Fit Methods
Numerical methods available for a Fit approach.
Method  Response Characteristics  Accuracy  Efficiency  Basic Parameters  Comments 

Fit Automatically Selected by Training  General  N/A  N/A  Choose methods for Fit Automatically Selected by Training to consider.  Selects the most appropriate method and settings. It it recommended that you use this method unless you desire a specific method and settings. 
HyperKriging  Interpolated data  ✩✩✩  ✩✩  The time to build the Fit and
use the Fit (Evaluate From)
increases with both the number of runs and the number of design
variables in the input matrix. The number of design variables has more influence than the number of runs if order is larger than 1. 

Least Squares Regression  Data trend lines  ✩  ✩✩✩  Noises can be screened out with this method. Closed form equations are available. 

Moving Least Squares Method (MLSM)  General  ✩✩  ✩✩  The time to build the Fit and
use the Fit (Evaluate From)
increases with both the number of runs and the number of design
variables in the input matrix. The number of design variables has more influence than the number of runs if order is larger than one. 

Radial Basis Function  Interpolate data  ✩✩✩  ✩✩  The time to build the Fit
increases with both the number of runs and the number of design
variables in the input matrix. The number of runs has more influence than the number of design variables. The run time for using the Fit in another approach (Evaluate From) is very small regardless of the size of the input matrix. 

VectorLSR  Predicting Vector Data  ✩  ✩✩✩  For each vector point LSR method is used. Information Matrix is assumed to be the same for every single point. 