What type of modeling tries to find rules for predicting variable values from other variables?

The focus of predictive modeling is to develop strategies and rules that allow for forecasting or predicting the values of a specific outcome variable based on the relationships identified with other variables. In this approach, historical data is utilized to analyze patterns and establish correlations that can then be employed to make informed predictions.

Predictive modeling typically involves techniques such as regression analysis, decision trees, and machine learning algorithms, all aimed at forming a reliable model which could be used for future predictions based on new or unseen data. This characteristic distinguishes it from other types of modeling, which may not prioritize prediction in the same manner.

Descriptive modeling, for example, aims to summarize and describe characteristics of a dataset without necessarily forming predictive rules. Exploratory modeling focuses on finding patterns and insights without specific hypotheses or predictions in mind, while diagnostic modeling seeks to determine causes of a particular outcome rather than merely predicting future occurrences. The essence of predictive modeling is to enable decision-makers to anticipate outcomes and trends, which is why it is the correct choice in this context.