In data mining, what does a confusion matrix show?

A confusion matrix is a critical tool used in machine learning and data mining, specifically in the evaluation of classification models. It presents a detailed breakdown of the model's performance by comparing the actual classifications (true classes) against the classifications predicted by the model.

The matrix includes four key components: true positives, true negatives, false positives, and false negatives. True positives indicate the number of instances correctly predicted as belonging to a positive class, while true negatives represent cases accurately identified as negative. False positives, on the other hand, are instances that were incorrectly classified as positive, and false negatives are those that were incorrectly classified as negative. By analyzing these values, one can derive a variety of performance metrics such as accuracy, precision, recall, and F1 score, providing insights into how well the classification model is functioning.

While the other options mention different contexts such as network data, user behavior, or data entry errors, none of these pertain to the specific function of a confusion matrix in assessing the efficiency of a classification algorithm in distinguishing between true and predicted classes.