What tool is used to visualize the results of a classification problem?

The confusion matrix is primarily used to visualize the results of a classification problem because it provides a detailed breakdown of the performance of a classification algorithm. It displays the actual versus predicted classifications, allowing practitioners to see how many instances were correctly classified (true positives and true negatives) and how many were misclassified (false positives and false negatives). This level of detail is crucial for assessing the accuracy and efficiency of the model, and it helps in calculating various performance metrics such as accuracy, precision, recall, and F1 score.

In contrast, the other tools listed serve different purposes. Histograms are used for visualizing the distribution of numerical data, box plots provide insights into the distribution characteristics like median and quartiles, and scatter plots are employed to illustrate relationships between two continuous variables. While these tools contribute to data analysis, they do not specifically focus on the evaluation of classification results like the confusion matrix does.