Why does fitcsvm support 'KFold' models only with fixed hyper-parameters?

Question

normanius 2018-5-8

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/399667-why-does-fitcsvm-support-kfold-models-only-with-fixed-hyper-parameters

评论： Don Mathis 2018-5-11

I would like to train a KFold model with modified hyper-parameters.

The following works:

            cvModel = fitcsvm(XTrain, yTrain, ...
                'KernelFunction', kernelType, ...
                'Weights', weights, ...
                'KFold', nPartitions);

However, the following does not:

            cvModel = fitcsvm(XTrain, yTrain, ...
                'KernelFunction', kernelType, ...
                'BoxConstraint', boxConstraint, ...
                'KernelScale', kernelScale, ...
                'PolynomialOrder', polynomialOrder, ...
                'Weights', weights, ...
                'KFold', nPartitions);

A model is trained, so the function completes without error, but it is not KFold. In the documentation , I noticed the following: To create a cross-validated model, you can use one of these four name-value pair arguments only: CVPartition, Holdout, KFold, or Leaveout.

So it is not allowed to use any other options than those named. How can I set BoxConstraint, KernelScale and other parameters and still yield a KFold model? It does not make sense to me at all why this should be prohibited!

Thanks for any support!

1 个评论
显示 -1更早的评论隐藏 -1更早的评论

Don Mathis 2018-5-11

在 MATLAB Online 中打开

I can't replicate your results. When I run your second code, I get a partitioned model.

Running this:

XTrain = rand(100,3);
yTrain = categorical(rand(100,1)>.5);
kernelType = 'polynomial';
boxConstraint = 1;
kernelScale = 1;
polynomialOrder = 2;
weights = rand(100,1);
nPartitions = 3;
cvModel = fitcsvm(XTrain, yTrain, ...
                'KernelFunction', kernelType, ...
                'BoxConstraint', boxConstraint, ...
                'KernelScale', kernelScale, ...
                'PolynomialOrder', polynomialOrder, ...
                'Weights', weights, ...
                'KFold', nPartitions)

Outputs this:

cvModel = 
    classreg.learning.partition.ClassificationPartitionedModel
      CrossValidatedModel: 'SVM'
           PredictorNames: {'x1'  'x2'  'x3'}
             ResponseName: 'Y'
          NumObservations: 100
                    KFold: 3
                Partition: [1×1 cvpartition]
               ClassNames: [false    true]
           ScoreTransform: 'none'
    Properties, Methods

请先登录，再进行评论。

请先登录，再回答此问题。

Answer 1

normanius 2018-5-8

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/399667-why-does-fitcsvm-support-kfold-models-only-with-fixed-hyper-parameters#answer_319248

在 MATLAB Online 中打开

A very related question: is the following equivalent?

cvModel1 = fitcsvm(XTrain, yTrain, ...
                   'KernelFunction', kernelType, ...
                   'Weights', weights, ...
                   'KFold', nPartitions);
cvModel2 = fitcsvm(XTrain, yTrain, ...
                   'KernelFunction', kernelType, ...
                   'Weights', weights);
cvModel2 = crossval(cvModel2, 'KFold', nPartitions);