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FRM Part I · FRM Exam Part I · Machine-Learning Methods

A risk analyst applies k-means clustering to group 500 corporate borrowers using financial ratios, without any default labels. Which statement best describes this approach?

K-means on borrower ratios with no default labels is unsupervised learning, because the algorithm looks for structure in the inputs without any target variable. Choosing k is only a hyperparameter, and the clusters it produces are outputs, not supplied labels.

  1. AIt is unsupervised learning because the algorithm finds structure in the data without a target variableCorrect
  2. BIt is supervised learning because the cluster assignments act as the target variable
  3. CIt is reinforcement learning because the algorithm is rewarded for tighter clusters
  4. DIt is supervised learning because the number of clusters k is specified in advance

Explanation

Clustering works only on input features and has no labeled outcome to predict, so it is unsupervised learning. Specifying k is a hyperparameter choice and does not make the method supervised. The cluster labels are produced by the algorithm, not supplied as targets.

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