What is a confusion matrix primarily used for?

A confusion matrix is primarily used to describe the performance of a classification model by summarizing the results of a classification task. It provides a detailed breakdown of predicted versus actual classifications, allowing practitioners to see where the model is performing well and where it is making errors. This visual representation includes metrics such as true positives, true negatives, false positives, and false negatives. By analyzing these values, one can derive various performance metrics, including accuracy, precision, recall, and F1 score, which are crucial for understanding and improving the model's effectiveness.

In contrast, options related to overall accuracy, visualization of data distribution, or simplifying model complexity do not accurately capture the specific purpose of a confusion matrix. While it can help infer overall accuracy, it is primarily focused on providing a comprehensive view of how well a classification model is working, making it an essential tool for evaluating classification performance.