What is the main benefit of using a confusion matrix to evaluate a machine learning (ML) model?

It helps analyze false positives and false negatives

Explanation

A confusion matrix helps in evaluating a classfication model by showing how many predictions were correct and incorrect, specifically analyzing false positives and false negatives.  it does not track model training time.  The number of data points used is separate from model evaluation.  Additional metrics like precision, recall and F1-score are still needed.