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CFA Level I · CFA Level I Exam · Introduction to Financial Data Science

A model achieves a very low error on its training data but a much higher error on a validation data set. This result most likely indicates that the model is:

The model is most likely overfit. It fits the training data, including noise, very closely, so it performs poorly on new data. Underfitting would produce high error on both training and validation data, and good generalization would produce similar errors on both sets.

  1. AoverfitCorrect
  2. Bunderfit
  3. Cwell generalized

Explanation

A large gap between low training error and high validation error shows the model has captured noise specific to the training sample and fails to generalize, which is overfitting. Underfitting would show high error on both sets. A well-generalized model would show similar errors on both.

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