Regularization - Ritvvij Parrikh Regularization | Ritvvij Parrikh Humane ClubMade with Humane Club

Regularization


Sometimes the model can overfit the training data and hence underperforms with real data. To reduce complexity, we use regularization.

  • Add a penalty factor on complexity i.e. number of features and values of weights.
  • Lasso: Force coefficients to zero if not relevant. Ends up performing feature selection.
  • Ridge: Reduces coefficients of irrelevant features but does not drop them to zero.
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