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

A data scientist builds a model that fits the training data almost perfectly but produces poor predictions on new data. The problem is most likely:

The problem is most likely overfitting. The model has learned noise specific to the training set, so it performs well in sample but poorly on new data. Techniques such as cross-validation and regularization help reduce it, whereas underfitting would also show poor training results.

  1. Aunderfitting, which could be reduced by removing features.
  2. Boverfitting, which could be reduced by using cross-validation and regularization.Correct
  3. Ca low variance error, which could be reduced by adding noise to the labels.

Explanation

A model that captures noise in the training data and fails out of sample is overfit. Cross-validation and regularization or simpler models help limit it. Underfitting would give poor training performance as well, so the first option is wrong.

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