Society of Actuaries (SOA) PA Practice Exam 2025 - Free Actuarial Practice Questions and Study Guide

Ensemble methods in model building are characterized by aggregating predictions from multiple models, which are often trained on different subsets of the data. This approach aims to enhance predictive performance and robustness compared to individual models. By using various subsets, ensemble methods can capture different patterns in the data, leading to more accurate and generalized predictions.

The strength of ensemble methods lies in their ability to reduce variance and bias, as combining the outputs of several models typically smooths out the inconsistencies or errors that may exist in any single model. This concept is foundational in machine learning, where techniques like bagging and boosting exemplify the ensemble approach.

While the other options mention aspects related to model building, such as using the same data or majority voting, they do not encapsulate the core principle of ensemble methods, which focuses on the aggregation of predictions from multiple models trained under diverse conditions.