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in Machine Learning by
How do you ensure you’re not overfitting with a model?

1 Answer

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This is a simple restatement of a fundamental problem in machine learning: the possibility of overfitting training data and carrying the noise of that data through to the test set, thereby providing inaccurate generalizations.

There are three main methods to avoid overfitting:

  1. Keep the model simpler: reduce variance by taking into account fewer variables and parameters, thereby removing some of the noise in the training data.
  2. Use cross-validation techniques such as k-folds cross-validation.
  3. Use regularization techniques such as LASSO that penalize certain model parameters if they’re likely to cause overfitting.

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