The Three Scenarios
🥶 Underfitting
Model is too simple - can't capture underlying patterns. Poor performance on both training and test data.
✨ Just Right
Model captures patterns well without memorizing. Good performance on both training and test data.
🔥 Overfitting
Model memorizes training data - can't generalize. Great on training data, poor on test data.
Key Indicators
How to Diagnose:
Training vs Validation Loss:
• Both high → Underfitting
• Large gap → Overfitting
• Both low, small gap → Just right
Learning Curves:
Plot training and validation performance over time
Quick Test: If your model performs much better on training data than validation data, you're overfitting!
Warning Sign: Perfect training accuracy (100%) is almost always overfitting in real-world problems.