What table is used to evaluate the performance of a classification model?

The confusion matrix is used to evaluate the performance of a classification model because it provides a comprehensive overview of the model's prediction results compared to the actual outcomes. It summarizes the classifications into a table, showing the counts of true positives, true negatives, false positives, and false negatives. By interpreting these values, you can calculate various performance metrics, such as accuracy, precision, recall, and F1 score, which are essential for understanding how well the model is performing in distinguishing between different classes.

The other options, while useful in their own contexts, do not specifically relate to evaluating classification model performance in the same comprehensive manner. For instance, box plots are primarily used for visualizing the distribution of numerical data and identifying outliers, scatter plots display relationships between two continuous variables, and heat maps can visualize matrix data or correlations but do not directly represent model classification results. Thus, the confusion matrix is the unique tool specifically designed for assessing classification outcomes.