How to avoid overfitting?

  • Cross-validation
  • 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.