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

A model predicting loan defaults achieves very low error on the training data but performs poorly on new data. This outcome is best described as:

This is best described as overfitting caused by excessive model complexity. The model has captured noise in the training data rather than the true underlying relationship, so it generalizes poorly to new data. Underfitting would instead produce weak results on training data as well.

  1. Aunderfitting caused by too few features
  2. Boverfitting caused by excessive model complexityCorrect
  3. Cdata leakage caused by a smaller validation sample

Explanation

Low training error with poor out-of-sample performance is the signature of overfitting: the model has learned noise specific to the training set, usually from excessive complexity. Underfitting would show poor performance on both sets. The third option does not describe this pattern.

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