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

Which description best distinguishes a classification problem from a regression problem in supervised learning?

Classification predicts a categorical outcome, such as whether a claim occurs, while regression predicts a numerical outcome, such as the claim amount. Both are supervised learning and use labelled data, so the difference lies only in the type of response variable being predicted.

  1. AClassification uses unlabelled data, whereas regression uses labelled data
  2. BClassification predicts a categorical outcome such as claim or no claim, whereas regression predicts a numerical outcome such as claim amountCorrect
  3. CClassification is always done with neural networks, whereas regression is always done with linear models
  4. DClassification groups similar inputs, whereas regression reduces the number of inputs
  5. Classification requires a reward signal, whereas regression does not

Explanation

Both are supervised and use labelled data. They differ in the response: categorical for classification, numerical for regression. The other options confuse them with unsupervised tasks, specific algorithms or reinforcement learning.

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