 | PLS |
Class PLS2CrossValidation performs an evaluation of a PLS (Partial Least
Squares) model.
Inheritance HierarchyNamespace: CenterSpace.NMath.Core
Assembly: NMath (in NMath.dll) Version: 7.4
SyntaxThe PLS2CrossValidation type exposes the following members.
Constructors| Name | Description | |
|---|---|---|
 | PLS2CrossValidation | Default constructor. Constructs a PLS2CrossValidation instance that uses the "leave one out" cross validation. LeaveOneOutSubsets |
| PLS2CrossValidation(ICrossValidationSubsets) | Constructs a PLS2CrossValidation instance which uses the given subset generator and the Nipals algorithm. |
| PLS2CrossValidation(IPLS2Calc, ICrossValidationSubsets) | Constructs a PLS2CrossValidation instance which uses the given PLS calculator and subset generator. |
Properties| Name | Description | |
|---|---|---|
 | AverageMeanSqrError | Gets the average of the mean square errors for each training/testing subsets pair. |
| Calculator | Gets and sets the PLS2 calculator to use for PLS2 calculations. |
| IsGood | Whether all the PLS2 calculations were successful. |
| Message | Gets any message that may have been generated by the computation. For example, if the calculation is unsuccessful, the message indicates the reason. |
| Results | Gets the results of the cross validation for each training/testing subsets pair. |
| SubsetGenerator | Gets and sets the subset generator to use to generate testing and training subsets. |
Methods| Name | Description | |
|---|---|---|
| Clone | Creates a deep copy of this PLS2CrossValidation. |
| DoCrossValidation(DoubleMatrix, DoubleMatrix, Int32) | Perform cross validation on the given data using the existing calculator and subset generator. |
| DoCrossValidation(DoubleMatrix, DoubleMatrix, IPLS2Calc, Int32) | Performs cross validation on the given data using the given calculator and number of components. |
Remarks
Evaluation consists of dividing the data into two
subsets - a training subset and a testing subset. A PLS calculation
is performed on the the training subset and the resulting model is
used to predict the values of the dependent variables in the testing
set. The mean square error between the actual and predicted dependent
values is then calculated. Usually, the data is divided up into several
training and testing subsets and calculations are done on each of these.
In this case the average mean square error over each PLS calculation
is reported (the individual mean square errors are available as well).
The subsets to use in the cross validation are specifed by providing
an implementation of the ICrossValidationSubsets interface. Classes
that implement this interface generate training and testing subsets
from PLS data.
See Also