Which types can predictors be classified as?

Predictors in decisioning models can be classified into various types based on the nature of the data they represent. The classification of predictors as NUMERIC and SYMBOLIC is particularly appropriate because it captures the two primary forms of data that can be used in predictive analytics.

NUMERIC predictors represent quantitative data that can take on a range of values. Examples include age, income, or scores, which can be mathematically manipulated and analyzed to derive insights or forecasts.

On the other hand, SYMBOLIC predictors refer to categorical data that classify observations into groups based on certain characteristics. These can include data types like strings or labels, such as customer segments, product categories, or yes/no responses. This classification allows for robustness in modeling non-numeric, qualitative factors that can significantly impact decision-making.

In this context, the classification of predictors into NUMERIC and SYMBOLIC enables practitioners to utilize a wide array of data types effectively, facilitating more comprehensive and accurate decisioning processes within Pega's decisioning frameworks.