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

Precision-Recall Curves. Sometimes a curve is worth a thousand…, by Doug Steen

Precision - Recall Curve, a Different View of Imbalanced Classifiers – Sin-Yi Chou – Data Science Enthusiast

Model evaluation for highly imbalanced datasets

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

PDF] PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R

Navigating Imbalanced Datasets: Beyond ROC & GINI

machine learning - Choose ROC/AUC vs. precision/recall curve? - Data Science Stack Exchange

Baseline ROC-AUC and Precision-Recall Curves for the RNA-Seq data.

Imbalanced Datasets: Complete Guide to Classification

Navigating Imbalanced Datasets: Beyond ROC & GINI

machine learning - Good ROC curve but poor precision-recall curve - Stack Overflow

Let's evaluate classification model with ROC and PR curves.