Classification and regression trees, pruning, and ensemble methods including bagging, random forests, and boosting.
These are the key learning objectives for Decision Trees on SOA Exam SRM. Paraphrased from the public SOA syllabus — we recommend also checking the current syllabus on soa.org before your exam sitting.
Construct and interpret classification and regression trees
Apply cost-complexity pruning to simplify decision trees
Explain and apply bagging, random forests, and boosted trees
Upload your ACTEX Exam SRM digital edition, scanned ASM pages, TIA handouts, or your own notes. exclam.ai extracts the Decision Trees sections and generates flashcards automatically.
Generate multiple-choice quizzes specifically on Decision Trees. Weak questions get re-surfaced until you get them right consistently.
Because Decision Trees is 20–25% of your exam, losing it during review costs you. FSRS brings it back at the optimal moment.
SOA Exam SRM has 5 topic areas. Decision Trees is weighted at approximately 20–25% of the exam — here is where it sits relative to the other topics.
| Topic area | Weight |
|---|---|
| Basics of Statistical Learning | 7–13% |
| Linear Models | 40–50% |
| → Decision Trees | 20–25% |
| Principal Components and Cluster Analysis | 5–10% |
| Time Series Models | 5–10% |
Supervised and unsupervised learning, model assessment, bias-variance tradeoff, and resampling methods including cross-validation.
Ordinary least squares regression, generalized linear models, variable selection, and regularization techniques.
Dimension reduction via principal components analysis and unsupervised clustering via k-means and hierarchical clustering.
Introduction to time series analysis including autoregressive, moving average, and ARIMA models.
Upload your ACTEX Exam SRM digital edition, scanned ASM pages, TIA handouts, or your own notes. exclam.ai generates a fully guided study plan with adaptive flashcards and quizzes for this topic.