How to count kfoldloss error from ClassificationLinear?

Question

Yean Lim 2020-12-7

0
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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/685353-how-to-count-kfoldloss-error-from-classificationlinear

编辑： Walter Roberson 2020-12-10

cv = cvpartition(numel(y_trainUndersampled),'Kfold',5); 
hyperOpt = struct('AcquisitionFunctionName','expected-improvement-plus',...  
            'Optimizer','bayesopt','MaxObjectiveEvaluations', 100,... 
            'CVPartition', cv); 
bestLogsMdl = fitclinear(X_trainUndersampled, y_trainUndersampled,... 
    'Learner', 'logistic',...
    'OptimizeHyperparameters',{'Lambda','Regularization'},...
    'HyperparameterOptimizationOptions',hyperOpt,...
    'ScoreTransform','logit');

Hi, I have used hyperparameter optimization on fitclinear function. The code above produces bestLogsMdl as ClassificationLinear.

I want to use ClassificationLinear to count the kfoldLoss.

However based on the documentation in https://uk.mathworks.com/help/stats/fitclinear.html#bu5mw4p , kfoldLoss is used on ClassificationPartitionedLinear

How to use hyperparameter optimization with fitclinear together on the kfoldLoss? What modifications are needed on the fitclinear so it would produce ClassificationPartitionedLinear?

My ultimate goal is to plot a misclassification rate vs number of learning cycles graph

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Answer 1

Walter Roberson 2020-12-8

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/685353-how-to-count-kfoldloss-error-from-classificationlinear#answer_569639

编辑：Walter Roberson 2020-12-10

https://www.mathworks.com/help/stats/fitclinear.html#namevaluepairarguments

You cannot use any cross-validation name-value pair argument along with the 'OptimizeHyperparameters' name-value pair argument. You can modify the cross-validation for 'OptimizeHyperparameters' only by using the 'HyperparameterOptimizationOptions' name-value pair argument.

So you need to get rid of OptimizeHyperParameters and set appropriate HyperparameterOptimizationOptions