ROC Curves and Precision-Recall Curves for Imbalanced Classification
Por um escritor misterioso
Last updated 31 março 2025

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 […]

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

ROC Curves and Precision-Recall Curves for Imbalanced Classification

a ROC curve and b precision-recall curve. The gray bands around the

Classification metrics and their Use Cases

Precision-Recall Curve in Python Tutorial

Imbalanced Datasets: Complete Guide to Classification

ROC and precision-recall with imbalanced datasets – Classifier evaluation with imbalanced datasets
The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets

ROC Curves and Precision-Recall Curves for Imbalanced Classification

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

Understanding AUC — ROC and Precision-Recall Curves, by Maria Gusarova

Precision-Recall curve explained. Learn to visualise a Precision-Recall…, by Zolzaya Luvsandorj

classification - Why are the ROC curves not smooth? - Cross Validated

Optimal Threshold for Imbalanced Classification, by Audhi Aprilliant

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