In machine learning, what is a confusion matrix?

A confusion matrix is fundamentally a table used to evaluate the performance of a classification model. It provides a detailed breakdown of the model's predictions compared to the actual outcomes. The matrix typically lists true positive, true negative, false positive, and false negative counts, allowing for the calculation of several key performance metrics, such as accuracy, precision, recall, and F1 score.

By providing a visual summary of the classification results, the confusion matrix facilitates the understanding of how well the model is distinguishing between different classes. It helps identify not only the overall performance but also specific patterns, such as which classes are being confused with each other, which can be valuable for refining the model or for data analysis.

For example, in a binary classification problem, a confusion matrix would display how many instances were correctly and incorrectly classified, enabling practitioners to assess the model's strengths and weaknesses in classifying the two classes. Thus, using a confusion matrix is an essential practice in evaluating classification models, making it the correct choice in this context.