How to get Precision, Recall,ROC,F_Mesure?

Hello anyone I want to get Precision, Recall, Sensitivity,Sprecificity,ROC. But I don't know how to implements code. i get Error at perfCurve and i don't know to fix it.

This my Train Code

imds = imageDatastore('Dataset', 'IncludeSubfolders', true, 'LabelSource', 'foldernames');
tbl = countEachLabel(imds);
minSetCount = min(tbl{:,2});
imds = splitEachLabel(imds, minSetCount, 'randomize');
countEachLabel(imds);
net = resnet50();
lgraph = layerGraph(net);
clear net;
numClasses = 2;
%numel(lgraph.Layers(end).ClassNames);
[trainingSet, testSet] = splitEachLabel(imds, 0.7, 'randomize');
imageSize = [224 224 3];
augmentedTrainingSet = augmentedImageDatastore(imageSize,...
    trainingSet, 'ColorPreprocessing', 'gray2rgb');
augmentedTestSet = augmentedImageDatastore(imageSize,...
    testSet, 'ColorPreprocessing', 'gray2rgb');
% New Learnable Layer    
newLearnableLayer = fullyConnectedLayer(numClasses, ...
    'Name','new_fc', ...
    'WeightLearnRateFactor',10,...
    'BiasLearnRateFactor',10);
% Replacing the last layers with new layers
lgraph = replaceLayer(lgraph,'fc1000',newLearnableLayer);
newsoftmaxLayer = softmaxLayer('Name','new_softmax');
lgraph = replaceLayer(lgraph,'fc1000_softmax',newsoftmaxLayer);
newClassLayer = classificationLayer('Name','new_classoutput');
lgraph = replaceLayer(lgraph,'ClassificationLayer_fc1000',newClassLayer);
options = trainingOptions('adam',...
    'MaxEpochs',6,'MiniBatchSize',8,...
    'Shuffle','every-epoch', ...
    'ValidationData', augmentedTestSet, ...
    'ValidationFrequency', 30, ... 
    'InitialLearnRate',1e-4, ...
    'Verbose',false, ...
    'Plots','training-progress');
netTransfer = trainNetwork(augmentedTrainingSet,lgraph,options);

And This my Test code for get the precision and any parameters

YPred = predict(netTransfer, augmentedTestSet); %imds_test is the image dastore containing the test images.

[Xpr,Ypr,Tpr,AUCpr] =perfcurve(testSet, newClassLayer, 1, 'xCrit', 'reca', 'yCrit', 'prec');

[c,cm,ind,per] = confusion(targets,outputs); %per represents the Matrix of percentages. Please refer to the doc for more details.

Can you help me to fix and get the data?.

Answer 1

[Deep]

The function "perfcurve" accepts 3 arguments: a vector of classifier predictions, given true class labels, and the positive class label. You can read more about its correct usage in detail from the official documentation by using the following command:

doc perfcurve

Additionally, since you are working with a confusion matrix, you might find the following MATLAB Answer by Sam helpful for calculating the desired performance metrics: https://www.mathworks.com/matlabcentral/answers/2053757-how-to-evaluate-the-performance-metrics-accuracy-precision-recall-f1score-on-the-tuned-fis#answer_1365099.