📊 Overfitting vs Underfitting Demo

Visualize the bias-variance trade-off in machine learning

❌ Underfitting (Too Simple)

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Train Error
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Test Error

✅ Good Fit (Just Right)

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Train Error
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⚠️ Overfitting (Too Complex)

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Test Error

🎓 Understanding the Bias-Variance Trade-off

❌ Underfitting (High Bias)

  • Model too simple
  • Poor training performance
  • Poor test performance
  • Doesn't capture patterns
  • Solution: Increase complexity

✅ Good Fit (Balanced)

  • Appropriate complexity
  • Good training performance
  • Good test performance
  • Generalizes well
  • Sweet spot! 🎯

⚠️ Overfitting (High Variance)

  • Model too complex
  • Excellent training performance
  • Poor test performance
  • Memorizes noise
  • Solution: Regularization, more data