How To Estimate FP, FN, TP, TN, TPR, TNR, FPR, FNR & Accuracy for Multi-Class Data in Python in 5 minutes

Author(s): Serafeim Loukas, PhD

Originally published on Towards AI.

In this post, I explain how someone can read a confusion matrix and how to extract several performance metrics for a multi-class classification problem from the confusion matrix in 5 minutes

In one of my previous posts, “ROC Curve explained using a COVID-19 hypothetical example: Binary & Multi-Class Classification tutorial”, I clearly explained what a ROC curve is and how it is connected to the famous Confusion Matrix. If you are not familiar with the term Confusion Matrix and True Positives, True Negatives, etc., refer to the above article and learn everything in 5 minutes or continue reading for a quick 2 minutes recap.

Let’s imagine that we have a test that is able within seconds to tell us if one individual is affected by the virus or not. So the output… Read the full blog for free on Medium.

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Published via Towards AI