Also known as: AUC (Area Under The ROC Curve) · AUC
El área bajo la curva ROC (AUC) es una métrica estándar de la industria para evaluar modelos de clasificación binaria a través de umbrales de decisión. Mide qué tan bien un modelo asigna puntuaciones más altas a ejemplos positivos que a negativos, con valores más cercanos a 1 que indican mayor discriminación y valores cercanos a 0,5 que indican poca capacidad de ordenación.
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.
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.
A side-by-side comparison of Area Under the ROC Curve and Area Under the PR Curve. Understand how ranking discrimination differs from precision-recall performance across thresholds.