What does a confusion matrix help analyze in a classification model?

A confusion matrix is a powerful tool used to evaluate the performance of a classification model by displaying the quantities of correct and incorrect predictions. It summarizes the results of a classification task by showing how many instances of each class were correctly classified and how many were misclassified.

In more detail, the confusion matrix provides a breakdown of the model's predictions into four categories: true positives, true negatives, false positives, and false negatives. This allows analysts to see not just the overall accuracy of the model, but also how well it performs for each class, which is crucial in cases where class imbalances exist or where certain classes are more significant than others. By analyzing the numbers in the confusion matrix, one can derive various metrics such as precision, recall, and F1 score, which give deeper insights into the model's predictive capabilities.

While the other options mention aspects related to data and model complexity, they do not directly pertain to the specific analysis facilitated by a confusion matrix. Therefore, option B is the most relevant and correct choice regarding the utility of a confusion matrix in assessing a classification model’s performance.