What does CoC stand for in predictive modeling?

In predictive modeling, CoC refers to the Coefficient of Concordance. This concept is primarily used to measure the degree of agreement between predicted values and actual outcomes in a modeling context. The Coefficient of Concordance assesses how well a predictive model ranks or orders predictions in relation to actual observed results. It is particularly important in scenarios where the goal is to predict binary or ordinal outcomes, helping practitioners to understand how effectively their model can rank instances according to their likelihood of an event occurring.

This metric is valuable as it goes beyond simple accuracy or error rates to provide insights into the model's discriminatory power, allowing analysts to refine their models based on how well they can predict certain outcomes. Understanding and applying the Coefficient of Concordance can significantly enhance a predictive model's performance and its interpretation in business decision-making processes.