B. A table used to evaluate the performance of a classification model

Tue, 05 May 2026 05:23:38 +0000

B. The Role of a Confusion Matrix in Evaluating Classification Model Performance In the field of machine learning, assessing the performance of a classification model is critical to ensuring its reliability and effectiveness in real-world applications. While various metrics—such as accuracy, precision, recall, and F1-score—help quantify model quality, the confusion matrix (often referred to as a B-matrix) stands out as a foundational tool for in-depth evaluation. This article explores what a confusion matrix is, how it supports model performance analysis, and why it remains an indispensable component in machine learning workflows. --- ### What Is a Confusion Matrix?