What is Overfitting?
Overfitting occurs when a machine learning model becomes too specialized in the training data, losing its ability to generalize to new, unseen examples. Imagine a student who memorizes their textbook but struggles with real-world problem-solving—the same happens to models!
How Does It Happen?
- Too Much Detail: The model learns noise and irrelevant patterns from the training data.
- Complexity Overload: An overly complex model fits the training data perfectly but fails on new data.
- Not Enough Data: With limited data, the model overfits to what it has, missing broader trends.
Detecting Overfit Models
- Cross-Validation: Split data into training and validation sets. If the model performs well on training but poorly on validation, it’s likely overfit.
- Regularization: Techniques like L1/L2 regularization prevent overfitting by penalizing complex models.
Remember: A good model balances fitting the data and generalizing to new situations. Keep it simple, smart, and adaptable!