What is the primary function of a confusion matrix?

The primary function of a confusion matrix is indeed to summarize the performance of a classification algorithm. It provides a detailed breakdown of the predictions made by the model, comparing them against the actual labels of the dataset. The matrix displays true positives, true negatives, false positives, and false negatives, which helps identify how well the model is performing in terms of precision and recall.

By analyzing these components, one can calculate various performance metrics such as accuracy, F1 score, and specificity, making it easier to understand where the model is succeeding and where it may need improvement. This is particularly important in classification tasks where understanding the nuances of different types of errors can provide insights into the model's behavior and effectiveness.