📘 Lesson · Lesson 89
Overfitting & Underfitting
Model Fitting Problems
💡 At a Glance
Overfitting = model memorizes training data but fails on new data. Underfitting = model is too simple to learn the pattern.
Comparison
| Problem | Training | New Data | Fix |
|---|---|---|---|
| Overfitting | very good | poor | more data, regularization, simpler model |
| Underfitting | poor | poor | more features, complex model, train longer |
Summary
- Overfitting: great on training, bad on new data — simplify or add data.
- Underfitting: bad everywhere — use a richer model or more features.