ROC-AUC

Lesson in the Machine Learning Roadmap · Stage 26: Metrics to Evaluate

Last updated 14 September 2026

What is ROC-AUC?

ROC-AUC, or Receiver Operating Characteristic Area Under the Curve, is a performance measurement for classification problems at various threshold settings. ROC is a probability curve that plots the True Positive Rate (TPR) against the False Positive Rate (FPR) at different threshold values. AUC measures the entire two-dimensional area underneath the entire ROC curve from (0,0) to (1,1). AUC provides an aggregate measure of performance across all possible classification thresholds.

Study this in the interactive course See the whole Machine Learning roadmap

Free resources to learn ROC-AUC

ArticleROC-AUC score | scikit-learn
ArticleAUC and the ROC Curve in Machine Learning
ArticleClassification: ROC and AUC

ROC-AUC: common questions

Can I learn ROC-AUC in Hindi?

This lesson is written in English. Once you sign in, open it in the interactive course and ask the AI tutor to explain ROC-AUC in Hindi or Hinglish. The tutor answers from this lesson’s own text, so it stays on the syllabus, and it runs on your own Google, OpenAI or Anthropic key.

Is this ROC-AUC tutorial for beginners?

It is lesson 102 of 150 in the free Machine Learning roadmap, in stage 26, Metrics to Evaluate. If you are new to Machine Learning, start from stage 1 and come back here in order; if you already know the earlier stages, this lesson stands on its own.