Final answer:
The performance of a classification model is often described with a confusion matrix, which is a specific type of table that shows true and predicted classifications enabling calculation of performance metrics.
Step-by-step explanation:
The term often used to describe the performance of a classification model applied to a set of test data for which the true outcomes are known is the confusion matrix. This is a table that summarizes the results of classification in terms of true positive, true negative, false positive, and false negative predictions. It allows us to calculate various performance metrics, such as accuracy, precision, recall, and F1 score, which can provide insights into the effectiveness of the model.
It is different from the other options because a parameter estimates table is associated with the values that parameterize a model, an ANOVA table is used for assessing the statistical significance of the factors in a model usually involving three or more group means, and an effect summary table summarizes the effect size and statistical significance of predictors within a model..