Probabilities from classification models can have two problems:
- Miscalibration: A p of .9 often doesn’t mean a 90% chance of 1 (assuming a dichotomous y). (You can calibrate it using isotonic regression.)
- Optimal cut-offs: For multi-class classifiers, we do not know what probability value will maximize the accuracy or F1 score. Or any metric for which you need to trade-off between FP and FN.
One way to solve #2 is to run the true labels and probabilities through a brute-force optimizer and gives you the optimal cut-off for the metric. Here’s the script for doing the same along with an illustration.