Understanding AUC in Machine Learning

The Area Under the Curve (AUC) measures the entire two-dimensional area under the ROC curve, providing a single value to compare model performance. Higher AUC values indicate better classification ability.

Step-by-Step Calculation

Collect False Positive Rate (FPR) and True Positive Rate (TPR) values at various thresholds
Sort points by FPR and ensure values range from 0 to 1
Apply the trapezoidal rule: AUC = Σ[(TPRₙ + TPRₙ₊₁) × (FPRₙ₊₁ - FPRₙ)] ÷ 2
Interpret results: AUC > 0.9 = excellent, 0.8-0.9 = good, 0.7-0.8 = fair

Reference Values

AUC Value	Interpretation
0.9 - 1.0	Excellent
0.8 - 0.9	Good
0.7 - 0.8	Fair
0.6 - 0.7	Poor
0.5 - 0.6	Fail

Tips & Warnings

Ensure FPR and TPR arrays have same length
Values must be between 0 and 1
AUC doesn't indicate optimal threshold - use Youden's J statistic
Not suitable for imbalanced datasets - consider Precision-Recall AUC

AUC Calculator - Fast Results

AUC Calculator

Understanding AUC in Machine Learning

Step-by-Step Calculation

Reference Values

Tips & Warnings

AUC Comparison Tool

Frequently Asked Questions

What does AUC measure?

Can AUC be greater than 1?

When should I not use AUC?

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Area Under the Curve AUC Calculator - Fast Results

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