In predictive model creation, what term refers to the fields used to determine the desired outcome?

In predictive model creation, the term that refers to the fields used to determine the desired outcome is "Predictors." Predictors are the specific variables or features in the dataset that are analyzed to influence or predict the target variable, which is the desired outcome. These predictors are statistical measurements or data inputs that provide valuable information which helps in building a model that can accurately forecast or make decisions based on new data.

To elaborate, predictors can include various types of data points such as demographic information, historical behavior, or any other relevant features that may help in identifying patterns or making predictions about future behaviors or outcomes. In a model, the relationship between these predictors and the outcome is analyzed to create predictive insights, allowing for informed decision-making.

The other terms listed in the choices, while related to data modeling, do not specifically capture the role played by predictors in this context. Outcomes refer to the result that the model aims to predict. Determinants and variables may describe elements of the dataset as well, but they do not precisely encompass the predictive role that predictors hold within model development. Thus, predictors stand out as the correct term in the context of defining the fields that contribute to forecasting outcomes in predictive modeling.