Train a multiclass ECOC classifier, and then cross-validate the model using a customk-fold loss function.

Load Fisher’s iris data set. Specify the predictor dataX, the response dataY, and the order of the classes inY.

loadfisheririsX = meas; Y = categorical(species); classOrder = unique(Y);% Class orderrng(1);% For reproducibility

Train and cross-validate an ECOC model using support vector machine (SVM) binary classifiers. Standardize the predictors using an SVM template, and specify the class order.

t = templateSVM('Standardize',1); CVMdl = fitcecoc(X,Y,'CrossVal','on',“学习者”,t,...'ClassNames',classOrder);

CVMdlis aClassificationPartitionedECOCmodel. By default, the software implements 10-fold cross-validation.

Compute the classification error (proportion of misclassified observations) for the validation-fold observations.

L = kfoldLoss(CVMdl)

L = 0.0400

Examine the result when the cost of misclassifying a flower asversicoloris10and the cost of any other error is1. Write a function namednoversicolorthat assigns a cost of1for general misclassification and a cost of10for misclassifying a flower asversicolor.

If you use the live script file for this example, thenoversicolorfunction is already included at the end of the file. Otherwise, you need to create this function at the end of your .m file or add it as a file on the MATLAB path.

Compute the mean misclassification error with thenoversicolorcost.

foldLoss = kfoldfun(CVMdl,@noversicolor); mean(foldLoss)

ans =single0.0667

This code creates the functionnoversicolor.

functionaverageCost = noversicolor(CMP,Xtrain,Ytrain,Wtrain,Xtest,Ytest,Wtest)% noversicolor: Example custom cross-validation function that assigns a cost of% 10 for misclassifying versicolor irises and a cost of 1 for misclassifying% the other irises. This example function requires the fisheriris data% set.Ypredict = predict(CMP,Xtest); misclassified = not(strcmp(Ypredict,Ytest));% Different resultclassifiedAsVersicolor = strcmp(Ypredict,'versicolor');% Index of bad decisionscost = sum(misclassified) +...9*sum(misclassified & classifiedAsVersicolor);% Total differencesaverageCost = single(cost/numel(Ytest));% Average errorend