Intermediate · Evaluation
Area under the PR curve
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. Area under the PR curve (PR AUC) measures a classifier's performance, especially for imbalanced datasets.
Technical Definition
Area under the PR curve (PR AUC) measures a classifier's performance, especially for imbalanced datasets.
How it works
The Area Under the Precision-Recall Curve (PR AUC) is a metric used to evaluate the performance of binary classification models. It plots precision against recall at various threshold settings, providing a single scalar value that summarizes the model's ability to discriminate between positive and negative classes, particularly useful when dealing with imbalanced datasets.
Related Concepts
- Classification — A supervised learning task where the model assigns inputs to discrete categories.
- Precision — Of the items the model predicted positive, the fraction that are actually positive.
- Recall — Of all the actually positive items, the fraction the model successfully found.
- Evaluation Metrics — Quantitative measures used to assess the performance, accuracy, and quality of AI models for specific tasks.