Poorly calibrated probabilities but good classification in confusion matrix


This Content is from Stack Overflow. Question asked by mapleleaf

I have an imbalanced data set. My goal is to balance sensitivity and specificity via the confusion matrix. I used glmnet in r with class weights. The model does well at balancing the sensitivity/specificity, but I looked at the calibration plot, and the probabilities are not well calibrated. I have read about calibrating probabilities, but I am wondering if it matters if my goal is to produce class predictions. If it does matter, I have not found a way to calibrate the probabilities when using caret::train().


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