There are many ways to avoid the overfitting of statistical models. The most common ways are:
- Train with more data to help the model detect the right signals.
- By removing irrelevant features from the model.
- Overfitting can also be avoided by preventing it at an early stage. In this, one needs to measure each iteration at all levels.
- Through regularization, overfitting can be avoided. In this solution, techniques to artificially force the model to be made simpler are used.
- Ensembling is another way to avoid overfitting data.