Overfitting happens when a machine learning model learns not just the underlying patterns but also the noise in the training data.
As a result, it performs very well on training data but poorly on new, unseen data.
It’s like memorizing answers instead of understanding concepts.
Causes of Overfitting
- Too complex models with many parameters
- Small or insufficient training data
- Noisy or irrelevant data
- Training for too many epochs
Signs of Overfitting
- High training accuracy but low test accuracy
- Predictions fail on new data
- Model is too sensitive to small changes in input
How to Prevent Overfitting
- Use more training data
- Simplify the model (fewer parameters)
- Regularization (L1, L2)
- Dropout (for neural networks)
- Early stopping during training
- Cross-validation
Real-World Examples
- Predicting house prices with too many irrelevant features
- Image classification where the model memorizes training images
- Stock prediction models failing on unseen data
Conclusion
Overfitting reduces a model’s ability to generalize. Preventing it ensures your machine learning models perform accurately on new, real-world data.
Citations
https://savanka.com/category/learn/ai-and-ml/
https://www.w3schools.com/ai/