Overfitting happens in a model when the machine learning algorithm strives to collect all of the data points and, as a result, catches noise as well. The method displays minimal bias but significant variation in the output due to this overfitting problem. One of the most serious problems in machine learning is overfitting.
Methods for avoiding Overfitting in Machine Learning:
- Cross-Validation
- Training With more data
- Regularization
- Ensembling
- Removing Unnecessary Features
- Early Stopping the training.