I wrote some codes to run a linear discriminant analysis based classification:
%% Construct a LDA classifier with selected features and ground truth information LDAClassifierObject = ClassificationDiscriminant.fit(featureSelcted, groundTruthGroup, 'DiscrimType', 'linear'); LDAClassifierResubError = resubLoss(LDAClassifierObject);
Thus, I can get
Resubstitution Error of LDA (Training Error): 1.7391e-01 Resubstitution Accuracy of LDA: 82.61% Confusion Matrix of LDA: 14 3 1 5
% Fit probabilities for scores (the groundTruthGroup contains lables either 'Good' or 'Bad') [FPR, TPR, Thr, AUC, OPTROCPT] = perfcurve(groundTruthGroup(:,1), LDAScore(:,1), 'Good');
I have got:
Therefore, we can get:
Accuracy of LDA after ROC analysis: 86.91% Confusion Matrix of LDA after ROC analysis: 13 1 2 7
My questions are:
1. After ROC analysis we obtained a better accuracy, when we report the accuracy of the classifier, which value we should use? What exactly the ROC curve can tell us or can be inferred? Can we say after ROC analysis we found a better accuracy of the LDA classifier?
2. Why the ROC can produce a better accuracy for the classifier, but the original ClassificationDiscriminant.fit can't?
3. I have also done a cross validation for the LDA classifier, like