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When to stop tuning machine learning models

When to stop tuning the hyperparameters in a machine learning model is a million-dollar question. This problem can be mostly solved by keeping tabs on training and testing errors. While increasing the complexity of a model, the following stages occur:

  • Stage 1: Underfitting stage - high train and high test errors (or low train and low test accuracy)
  • Stage 2: Good fit stage (ideal scenario) - low train and low test errors (or high train and high test accuracy)
  • Stage 3: Overfitting stage - low train and high test errors (or high train and low test accuracy)
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