Area Under The ROC Curve (AUC)

Also known as: AUC (Area Under The ROC Curve) · AUC

Caesar AI Atlas Definition

Area Under The ROC Curve (AUC) is an industry-standard metric for evaluating binary classification models across decision thresholds. It measures how well a model assigns higher scores to positive examples than to negative examples, with values closer to 1 indicating stronger discrimination and values near 0.5 indicating little ranking ability.

Other Definitions

Area Under The ROC Curve Source

See AUC (Area under the ROC curve).

Area Under The ROC Curve Source

Area Under a Curve. An industry-standard metric to evaluate the quality of a binary classification machine learning model. AUC measures the ability of the model to predict a higher score for positive examples than for negative examples. The AUC metric returns a decimal value from 0 to 1. AUC values near 1 indicate an ML model that's highly accurate.

Concept Comparisons

MetricsEvaluation

F1 Score vs Area Under the ROC Curve

A side-by-side comparison of F1 Score and Area Under the ROC Curve. Understand how a threshold-dependent balance of precision and recall differs from ranking discrimination across thresholds.