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

A risk analyst fits a very flexible model to predict loan defaults. The model achieves almost perfect accuracy on the training data but performs poorly on a held-out validation sample. Which statement best describes this outcome?

The model is overfitted and has high variance. It has captured noise in the training data, so it performs very well in-sample but poorly on new data. Underfitting would instead show weak performance on both the training and validation samples.

  1. AThe model is overfitted and has high varianceCorrect
  2. BThe model is underfitted and has high bias
  3. CThe model has low variance and high bias
  4. DThe model is well calibrated and generalizes well

Explanation

A large gap between strong training performance and weak out-of-sample performance is the signature of overfitting. The model has fitted noise specific to the training sample, which means high variance. Underfitting would show poor performance on both training and validation data.

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