IAI Actuarial Core Principles · Risk Modelling and Survival Analysis · Elementary principles of machine learning
Which of the following is an example of a regression task, as opposed to a classification task, in a supervised learning setting?
Predicting the rupee amount of a claim is regression because the target is a continuous numerical value. Lapse prediction and severity bands are classification, while clustering and principal components analysis are unsupervised techniques with no target.
- APredicting whether a policyholder will lapse (yes or no)
- BAssigning a claim to one of three severity bands
- CPredicting the rupee amount of a claim from policy featuresCorrect
- DGrouping policyholders into similar clusters
- Reducing 40 rating factors to two principal components
Explanation
Regression predicts a continuous numerical target, such as a claim amount. Lapse and severity band are categorical outcomes (classification), while clustering and principal components are unsupervised.
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 …
- A model predicting whether a policy lapses is tested on 1,000 policies, of which 100 actually lapse. The model predicts lapse for 80 policie…
- 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…