Society of Actuaries PA Practice Exam Study Guide

Partial Dependence Plots (PDPs) are specifically designed to visualize the relationship between a subset of features and the predicted outcome of a machine learning model, while averaging out the effects of all other features. This means they help illustrate how changes in specific features influence the predicted response, providing insight into the model's dependence on those selected features.

By isolating the effect of one or more predictor variables, PDPs make it easier for practitioners to understand the features that significantly impact predictions, thereby enhancing interpretability of complex models. With a clear graphical representation, users can see trends and patterns in the model’s predictions as the selected feature values change, which does not effectively capture trends in training data, assess model performance, or show class distribution.