Overfitting is a situation that occurs in statistical modeling or Machine Learning where the algorithm starts to over-analyze data, thereby receiving a lot of noise rather than useful information. This causes low bias but high variance, which is not a favorable outcome.
Overfitting can be prevented by using the below-mentioned methods:
- Early stopping
- Ensemble models
- Cross-validation
- Feature removal
- Regularization