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IAI Actuarial Core Principles · Risk Modelling and Survival Analysis · Elementary principles of machine learning

A model fitted to a training set gives a very low training error but a much higher error on a separate test set. Which is the most likely explanation?

The model is most likely overfitted. It has captured noise in the training data, so training error is low, but it generalises poorly to unseen data, giving a much higher test error. Underfitting would show high error on both sets.

  1. AThe model is overfitted to the training dataCorrect
  2. BThe model is underfitted because it is too simple
  3. CThe test set has been used to fit the parameters
  4. DThe training set is too large for the model
  5. The model has high bias and low variance

Explanation

A large gap between low training error and high test error is the classic sign of overfitting: the model has learned noise specific to the training sample and does not generalise. Underfitting would give high error on both sets.

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