Society of Actuaries (SOA) PA Practice Exam 2025 – 400 Free Practice Questions to Pass the Exam

Utilizing ensemble methods is a powerful technique for increasing the robustness of a model when dealing with noise in the data. Ensemble methods combine the predictions of multiple base models to improve overall performance and reduce the likelihood of overfitting to noise or outliers.

One common ensemble method is bagging (e.g., Random Forest), which builds multiple decision trees on different subsets of the training data, allowing the model to average out individual errors that may arise from noisy data. This averaging effect helps to smooth out the influence of noise and leads to better generalization on unseen data.

Another ensemble technique is boosting, which focuses on constructing a series of models that learn from the mistakes of previous ones, effectively honing in on the areas of the data where errors occur, also providing robustness against noise by emphasizing the correction of weak learners.

In contrast, relying solely on a single decision tree would not adequately handle noise since it can be highly sensitive to variance in the data. Regularization techniques, while effective in controlling overfitting, primarily pertain to specific models (like linear regression) and may not inherently address the issue of noise across diverse datasets. Logistic regression may offer a solid performance for binary classification tasks, but it doesn’t inherently improve robustness against noisy inputs like ensemble methods