ROC curve in machine learning

Introduction

Laymen explanation

Technical explanation

In Machine Learning, performance measurement is an essential task. So when it comes to a classification problem, we can count on an AUC - ROC Curve.

Abbreviation

AUC (Area Under The Curve)

ROC (Receiver Operating Characteristics)

An excellent model has AUC near to the 1 which means it has a good measure of separability.

A poor model has an AUC near 0 which means it has the worst measure of separability. In fact, it means it is reciprocating the result. It is predicting 0s as 1s and 1s as 0s. And

when AUC is 0.5, it means the model has no class separation capacity whatsoever.

Reference

https://towardsdatascience.com/understanding-auc-roc-curve-68b2303cc9c5

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