What best describes the term 'classification table' in predictive analytics?

The term 'classification table' refers specifically to a tool that summarizes the predicted classifications versus actual outcomes. This table is crucial in the context of predictive analytics because it allows you to evaluate the performance of a classification model. By displaying these predicted classifications side by side with the actual outcomes, it facilitates the calculation of various performance metrics such as accuracy, precision, recall, and the overall effectiveness of the model.

The classification table is commonly known as a confusion matrix, which helps in identifying the strengths and weaknesses of the predictive model, thus guiding further refinement or adjustment of the model if necessary. Understanding how many predictions were correct and where errors occurred is fundamental for improving predictive accuracy in decision-making processes.

Other options, while related to data analysis, do not directly address the specific function and importance of a classification table in evaluating model performance.