What statement is accurate about scoring models?

Scoring models are designed to evaluate and predict outcomes based on input data and can handle multiple categories effectively. The correct statement indicates that scoring models may have more than two categories, reflecting their versatility in classifying and predicting behaviors or outcomes. This capability allows organizations to segment their analysis and derive insights from complex data sets, as scoring models can accommodate a range of categorical variables beyond binary classifications.

In contrast, some other options suggest limitations that do not accurately reflect the capabilities of scoring models. Stating that scoring models are best suited for discrete behavior implies a restriction to specific types of outcomes, whereas these models are effective across various types, including continuous and categorical data. The notion that scoring models can only handle one category per symbol mapping suggests a lack of flexibility; in practice, multiple categories can be represented and analyzed within a single scoring model framework. Lastly, claiming that scoring models cannot use numerical predictors overlooks their capability to integrate and leverage numerical data, which is often essential for robust predictive analytics.