ROC Curves and Precision-Recall Curves for Imbalanced Classification

Most imbalanced classification problems involve two classes: a negative case with the majority of examples and a positive case with a minority of examples. Two diagnostic tools that help in the interpretation of binary (two-class) classification predictive models are ROC Curves and Precision-Recall curves. Plots from the curves can be created and used to understand […]

The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets

machine learning - Can Precision-Recall be improved for imbalanced sample? - Data Science Stack Exchange

The area under the precision‐recall curve as a performance metric for rare binary events - Sofaer - 2019 - Methods in Ecology and Evolution - Wiley Online Library

Measuring Performance: AUPRC and Average Precision – Glass Box

Imbalanced Datasets: Complete Guide to Classification

ROC and precision-recall with imbalanced datasets – Classifier evaluation with imbalanced datasets

Navigating Imbalanced Datasets: Beyond ROC & GINI

Handling Class Imbalance with R and Caret - Caveats when using the AUC

How to Choose the Best Evaluation Metric for Classification Problems, by Thomas A Dorfer