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IAI Actuarial Core Principles · Risk Modelling and Survival Analysis · Elementary principles of machine learning

A life insurer in Mumbai has data on 20,000 customers (age, premium paid, number of policies, channel) but no outcome variable. An analyst applies k-means to find groups of similar customers. Which statement about this exercise is correct?

It is unsupervised learning. No outcome variable is supplied, and k-means groups customers by similarity of their features, discovering the structure from the data itself. Since the groups are not known labels beforehand and there is no reward signal, it is neither supervised nor reinforcement learning.

  1. AIt is unsupervised learning because no target variable is provided and the aim is to find structure in the inputsCorrect
  2. BIt is supervised learning because the groups found act as labels known in advance
  3. CIt is reinforcement learning because the algorithm improves through repeated rewards
  4. DIt is supervised regression because the cluster centres are continuous
  5. It cannot be machine learning because it does not predict anything

Explanation

K-means searches for structure in the inputs without any response variable, so it is unsupervised learning. The clusters are discovered, not given beforehand, so the supervised option is wrong. There is no reward signal, so it is not reinforcement learning.

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