Hi,
To achieve the goal of getting separate losses (or True Positive Rates, TPR) for each class in a binary classification problem using k-fold cross-validation, you can calculate the TPR for each class manually after predicting the class labels on the validation set in each fold. The "kfoldPredict" function in MATLAB can be utilized to obtain the predictions for each fold. These predictions can then be compared to the true labels to calculate the TPR for each class.
Here is how you can modify your code snippet for one of the models to include TPR calculation for both classes:
% Initialize matrices for TPRs for both classes
indTPRClass1 = zeros(10, 6);
indTPRClass2 = zeros(10, 6);
% Example for FineKNN model
FineKNN = fitcknn(FV, Lbl, 'CVPartition', Partitions, 'NumNeighbors', 1, 'Standardize', 1);
predictions = kfoldPredict(FineKNN);
for fold = 1:10
% Extract the test indices for this fold
testIdx = test(Partitions, fold);
trueLabels = Lbl(testIdx);
predLabels = predictions(testIdx);
% Calculate TPR for each class
indTPRClass1(fold, 1) = sum(predLabels == 1 & trueLabels == 1) / sum(trueLabels == 1);
indTPRClass2(fold, 1) = sum(predLabels == 0 & trueLabels == 0) / sum(trueLabels == 0);
end
% Repeat similar steps for other models and fill in indTPRClass1 and indTPRClass2 accordingly
To learn more about “kfoldPredict” usage and syntax, you may refer to the link below: -
I hope this helps!
