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FRM Part I · FRM Exam Part I · Machine Learning and Prediction

A data scientist fits a model with very low training error but much higher error on a held-out validation set. Which is the most appropriate conclusion and response?

The model is overfitting. Low training error with much higher validation error means it has learned noise rather than the underlying pattern. Appropriate responses include regularization, a simpler model, or more data, while underfitting would show poor performance on both sets.

  1. AThe model is underfitting; add no regularization and reduce features
  2. BThe model is overfitting; use regularization, simplify the model, or gather more dataCorrect
  3. CThe model is unbiased; the validation set should be discarded
  4. DThe model has high bias; increase its complexity

Explanation

A large gap between low training error and high validation error signals overfitting, where the model fits noise. Remedies include regularization, simpler models, cross-validation, or more data. Underfitting would show high error on both sets.

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