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

The purpose of a confusion matrix in model evaluation is to summarize model predictions by providing a detailed breakdown of the performance of a classification model. It presents a two-dimensional table that displays the counts of true positives, true negatives, false positives, and false negatives. This allows for an evaluation of the model's accuracy, precision, recall, and F1 score, among other metrics. By analyzing the confusion matrix, one can gain insights into not only how many predictions were correct but also the types of errors being made, which can inform decisions on model improvements or adjustments.

The other options do not align with the main purpose of a confusion matrix. For example, optimizing memory usage relates to system efficiency rather than model evaluation. Visualizing a decision tree pertains to understanding the structure of decision-making within a model, which is unrelated to the confusion matrix's function. Testing the robustness of data focuses on examining the validity of the data itself, not on evaluating the model's performance.