Ensemble Methods: Stacking and Blending for Actuaries

Beyond Simple Ensembles

While bagging and boosting are well-known ensemble methods, stacking and blending take model combination further. Stacking trains a meta-learner on the predictions of base models, allowing it to learn optimal ways to combine them. Blending uses a holdout set instead of cross-validation for the meta-learner training. Both methods can capture strengths of diverse model types.

Actuarial Applications

In insurance pricing, a stack might combine a GLM (for interpretability and regulatory compliance), a gradient boosting machine (for predictive power), and a neural network (for capturing complex interactions). The meta-learner, often a simple linear model, assigns weights based on each model's contribution. For MAS-II, understand how to set up cross-validated stacking to avoid data leakage, when blending is preferred over stacking, and the bias-variance trade-off in ensemble construction. Be prepared to discuss practical concerns like model governance and explaining ensemble predictions to regulators.