Overfitting in machine learning is a problem in which the ML model fails to generalize well to unseen data because its predictions very tightly much the training data. An overfitting model has  high variance and low bias. Overfitting is the other pole to underfitting. The desired situation between overfitting and underfitting is a good fit (or best fit).

The most common causes of overfitting are the following:

  • High variance and low bias.
  • The model is too complex.
  • The model is non-parametric and non-linear. Non-parametric ML models generally have overfitting behavior.
  • The size of the training data is small or there is too much noise in the training data.
  • Lack of regularization.

The following methods can be used to reduce overfitting:


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