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ROC Curve Comparison

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Description

Compare ROC curves for classifiers with different performance levels.

Learning Objectives

  • Understand how ROC curves visualize classifier performance
  • Interpret AUC (Area Under Curve) as a performance metric
  • Compare multiple classifiers using ROC curves
  • Recognize the trade-off between TPR and FPR

How to Use

  1. Select Classifier: Choose performance level from dropdown
  2. Show All: Toggle to compare all classifiers simultaneously
  3. Observe: See how AUC relates to curve position

Key Concepts

ROC Curve

  • Plots True Positive Rate (TPR) vs False Positive Rate (FPR)
  • Shows performance at all classification thresholds
  • Better classifiers curve toward top-left corner

AUC (Area Under Curve)

  • Single metric summarizing classifier performance
  • Range: 0.5 (random) to 1.0 (perfect)
  • Higher AUC = better overall performance

Interpreting Performance

  • Excellent: AUC > 0.9
  • Good: AUC 0.8-0.9
  • Fair: AUC 0.7-0.8
  • Poor: AUC 0.5-0.7
  • Random: AUC = 0.5 (diagonal line)