Machine Learning Foundations8 min read• Published August 09, 2026

ROC-AUC Curves, PR-AUC, and Multi-Class Evaluation Metrics

Understanding decision threshold curves, Area Under Curve interpretation, PR-AUC vs ROC-AUC on imbalanced datasets, and Macro vs Micro averaging.

GS
Govindarajan Selvaraj
ML Engineer

⚡ Executive Summary

Explore threshold-agnostic ROC curves, probability ranking, Precision-Recall AUC for rare classes, and multi-class macro, micro, and weighted averaging techniques.

Key Takeaways

  • ✓ROC-AUC — covered in depth with practical examples, formulas, and code.
  • ✓Multi-class Extensions — covered in depth with practical examples, formulas, and code.

ROC-AUC Curves & Multi-Class Evaluation Guide

#1. ROC-AUC

ROC (Receiver Operating Characteristic) curve plots:

  • X-axis: False Positive Rate = FP / (FP + TN)
  • Y-axis: True Positive Rate (Recall) = TP / (TP + FN)

    • ...at every possible classification threshold (0.0 to 1.0), instead of just one fixed threshold.

    AUC (Area Under the Curve) condenses the curve into one number from 0 to 1:

  • 1.0 = perfect classifier
  • 0.5 = random guessing (diagonal line)
  • < 0.5 = worse than random (model is inverted)

    • Interpretation: AUC is the probability that the model ranks a random positive example higher than a random negative example.

    When to use: Comparing models independent of a chosen threshold, or when you'll tune the threshold later. Caution: Can be misleading on heavily imbalanced datasets — use PR-AUC (Precision-Recall AUC) instead in that case, since it doesn't reward performance on the (huge) negative class the way ROC-AUC can.

    python
    from sklearn.metrics import roc_auc_score
roc_auc_score(y_true, y_pred_proba)  # needs probabilities, not hard labels

    #2. Multi-class Extensions

    Precision/Recall/F1 don't have one obvious definition beyond two classes, so they're computed per-class and averaged:

  • Macro average — average metric across classes, unweighted (treats rare and common classes equally)
  • Micro average — aggregate TP/FP/FN across all classes first, then compute (dominated by common classes)
  • Weighted average — average weighted by class support (frequency)
    • python
    from sklearn.metrics import classification_report
print(classification_report(y_true, y_pred, target_names=['Class A', 'Class B', 'Class C']))
    Tags:#ROC-AUC#PR-AUC#Multi-Class#Machine Learning#Model Evaluation
    ABOUT THE AUTHOR
    GS

    Govindarajan Selvaraj

    ML Engineer

    Govindarajan Selvaraj is part of the Junglans Solutions engineering team, specializing in machine learning foundations. Junglans builds a 20-product ecosystem of local-first enterprise software — AI developer tools, encrypted communication, and data infrastructure with zero cloud telemetry.

    Meet the full Junglans engineering team ↗
    RELATED RESOURCES & REFERENCES

    This article is part of the Junglans Research knowledge base, produced alongside the engineering teams that build our production AI tools. Explore related product documentation and research:

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