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

A data scientist increases the complexity of a prediction model by adding many more predictors and higher-order terms. Holding the training sample fixed, what is the typical effect on the model's bias and variance of its predictions?

Bias typically falls and variance rises. A more complex model can capture the true relationship more closely, reducing systematic error, but its predictions become more sensitive to the specific training sample, increasing variance. This is the core bias-variance tradeoff.

  1. ABias increases and variance decreases
  2. BBias decreases and variance increasesCorrect
  3. CBoth bias and variance decrease
  4. DBoth bias and variance increase

Explanation

More flexible models can approximate the true relationship more closely, which lowers bias. However, their fitted predictions become more sensitive to the particular training sample, so variance rises. This is the bias-variance tradeoff.

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