What is the purpose of a confusion matrix?

The purpose of a confusion matrix is to provide a comprehensive assessment of a classification model's performance by displaying the counts of true positives, true negatives, false positives, and false negatives. This matrix allows one to evaluate how well the model distinguishes between different classes in the dataset.

In a binary classification context, the true positives represent the instances that were correctly predicted as positive, while true negatives are those correctly predicted as negative. Conversely, false positives indicate instances that were incorrectly predicted as positive, and false negatives identify those that were incorrectly predicted as negative. By analyzing these four categories, one can derive important metrics such as accuracy, precision, recall, and the F1 score, which are essential for understanding the effectiveness of the classification model.

While visualizing data distributions, evaluating regression model performance, and determining data cleaning techniques are important aspects of data analytics, they do not specifically pertain to the role of a confusion matrix, which is solely focused on the outcomes of classification tasks. This makes the use of the confusion matrix particularly relevant in settings where the distinction between classes is critical.