In a confusion matrix, what do true positives represent?

True positives in a confusion matrix represent the instances that are accurately predicted as positive. This means that the model has successfully identified the positive class when it is indeed the case. In practical terms, if a model is designed to classify whether an email is spam or not, true positives would refer to the emails that are both classified as spam by the model and are actually spam.

Understanding this concept is crucial in evaluating a model's performance, as it helps to measure the model's ability to identify the relevant class among the total instances. A high number of true positives indicates that the model is effective in predicting the positive instances correctly, which is fundamental for tasks that require accurate classification, especially in areas such as medical diagnosis, fraud detection, and sentiment analysis.