Hyper-parameter optimization
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Elena Casiraghi
le 21 Sep 2019
Réponse apportée : Elena Casiraghi
le 24 Sep 2019
1) When training an ECOC classifier for multiclass classification, with knn as base learner, how can I change the minimized function (from the classification error to a loss function I want to define)?
I'm now using this code (where the loss function is in the last line of code). If Preds are the predicted classes, labels are the true classes, N is the numebr of sample, my loss is:
myLoss = double(sum(abs(resPreds - labels)))/double(N); % this is the loss function I wish to minimize
% variable labels contains the labels of training data
tknn = templateKNN('Distance', @distKNN); % I WOULD LIKE TO USE THIS DISTANCE
N = size(XKnn,1);
c = cvpartition(N,'LeaveOut');
% Use leave one out
mdlknnCecoc = fitcecoc(XKnn,labelsRed, ...
'OptimizeHyperparameters','auto', ...
'HyperparameterOptimizationOptions',struct( 'UseParallel',...
true,'CVPartition',c), 'Learners',tknn);
resPreds = predict(mdlknnCecoc, XKnn); % I don't know why kfoldPredict function does not work
myLoss = double(sum(abs(resPreds - labels)))/double(N); % this is the loss function I wish to minimize
2 commentaires
Don Mathis
le 23 Sep 2019
Is it important for you to use ECOC for this? fitcknn directly supports multiclass problems.
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