Intermediate · Evaluation

ROC Curve

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. A plot of true-positive rate against false-positive rate across all classification thresholds.

Technical Definition

A plot of true-positive rate against false-positive rate across all classification thresholds.

How it works

The ROC curve visualizes the trade-off available by moving the decision threshold. A perfect classifier hugs the top-left corner; random guessing falls along the diagonal. AUC summarizes the curve in a single number.

Related Concepts

• Precision & Recall — Complementary metrics measuring classifier accuracy on positive predictions (precision) and ability to find all positives (recall).
• Accuracy — The fraction of predictions a model gets correct on a labeled dataset.
• AUC — Area Under the ROC Curve — a threshold-independent measure of classifier quality.