Society of Actuaries PA Practice Exam Study Guide

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What is the primary objective of Best Subset Selection in regression analysis?

To assess the predictive capability of a model

To find the subset of independent variables that best predict the outcome

The primary objective of Best Subset Selection in regression analysis is to find the subset of independent variables that best predict the outcome. This approach involves evaluating different combinations of predictor variables to identify the set that provides the most accurate predictive power for the dependent variable. By systematically considering all possible subsets of the available independent variables, the method aims to optimize predictive accuracy while thus enhancing the model's interpretability by using only the most relevant predictors.

Finding the right subset is crucial because including too many variables can lead to overfitting, where the model captures the noise in the data rather than the underlying relationship. By focusing on the subset that provides the best fit for the data, Best Subset Selection facilitates more effective and reliable predictions of future observations based on the chosen predictors.

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To evaluate the overall performance of all combinations of variables

To limit the number of independent variables utilized in the model

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