IAI Actuarial Core Principles · Risk Modelling and Survival Analysis · Elementary principles of machine learning
A general insurer in India wants to group its motor policyholders into segments using only rating variables such as vehicle age, engine capacity and annual mileage, with no claim-cost label used in the analysis. Which description best fits this task?
This is unsupervised learning. The insurer groups policyholders using only explanatory features and no response or label, so the algorithm discovers the segments itself. Classification and regression need a known target, and there are no rewards or partial labels here.
- AUnsupervised learning, because no target variable is used to guide the groupingCorrect
- BSupervised classification, because the segments act as known class labels
- CReinforcement learning, because the insurer rewards good segments
- DSupervised regression, because mileage is a continuous variable
- Semi-supervised learning, because some policies carry claim amounts
Explanation
Clustering looks for structure in the features alone without a response variable, which is the definition of unsupervised learning. The segments are discovered, not given, so classification is wrong. Nothing in the problem involves rewards or partially labelled data.
Did you get it right without looking?
One question tells you little. A timed set on Elementary principles of machine learning shows your real accuracy, how long you take and where you lose marks.
More Elementary principles of machine learning questions
- A health insurer groups 50,000 customers into segments using only age, income and claim frequency, with no predefined segment labels. Which …
- When building a predictive model for insurance data, which action best reduces the risk of data leakage?
- A pricing team fits a claim-severity model and finds that its error on the data used for fitting is very small, but its error on a separate …
- A modeller fits polynomial regressions of increasing degree to the same training data. The training error falls steadily with degree, but th…
- In ridge regression, a penalty equal to a positive constant λ times the sum of squared coefficients is added to the residual sum of squares.…
- A dataset is split into training, validation and test sets to choose the number of neighbours k in a k-nearest-neighbours model. What is the…