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

A data scientist builds a model with many features and finds it fits the training set almost perfectly but performs poorly out of sample. Which action is most likely to improve the model's out-of-sample performance?

Applying regularization to penalize model complexity is most likely to help. The model is overfit, so reducing complexity lowers variance and improves generalization. Adding features makes overfitting worse, and using a smaller training set gives less information for learning real patterns.

  1. AAdd more features to capture remaining training-set variation
  2. BApply regularization to penalize model complexityCorrect
  3. CReduce the size of the training set to speed up fitting

Explanation

The symptoms indicate overfitting. Regularization (penalizing complexity, such as with LASSO) or fewer features reduces variance and improves generalization. Adding features increases complexity and worsens overfitting, and shrinking the training set gives the model less information, which also tends to worsen it.

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