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Metrics

Explore common metrics used to evaluate binary classification models such as true positive rate, false positive rate, precision, recall, and accuracy. Understand their meanings, how to calculate them using confusion matrices, and the trade-offs involved in optimizing these metrics for different applications.

Most commonly used metrics

Starting with these four numbers, TN, FP, FN, and TP, you may construct a ton of metrics. We are focusing here on the most commonly used: true and false-positive rates (TPR and FPR), precision, recall, and accuracy.

True and false-positive rates

Let us start with the first two:

TPR=TPTP+FNTPR = \dfrac{TP}{TP + FN}   FPR=FPFP+TN\space \space FPR = \dfrac{FP}{FP + TN} ...