What is the purpose of a confusion matrix in classification?

A confusion matrix is a valuable tool used to evaluate the performance of a classification model. It provides a comprehensive summary of the true versus predicted classifications by displaying the counts of true positives, true negatives, false positives, and false negatives. This matrix helps to determine how well the model is making predictions by showing where it is performing accurately and where it is making errors.

By analyzing the values within the confusion matrix, one can derive various performance metrics such as accuracy, precision, recall, and F1 score, all of which provide insight into the model's strengths and weaknesses. For instance, high values in the true positive and true negative categories indicate a well-performing model, while high false positive or false negative values suggest areas needing improvement.

In contrast, visualizing data distributions or summarizing dataset statistics does not provide this level of insight into model performance and predictions. Computing correlations between features focuses on relationships within the dataset itself rather than evaluating the actual outcomes of a classification model. Thus, the confusion matrix specifically addresses the evaluation aspect of classification models, making it an essential component in assessing their effectiveness.