A confusion matrix, also known as an error matrix, is a fundamental tool in the realm of machine learning and statistics, specifically for evaluating the performance of classification models. It provides a detailed breakdown of a model's predictions compared to the actual outcomes, allowing for a granular analysis of where the model is performing well and where it's making errors.
The term "confusion" in "confusion matrix" stems from its primary purpose: to show where the model is "confused" in its classifications. By analyzing the matrix, one can discern between the types of correct and incorrect predictions a model makes.