What term is used for predictors, features, explanatory variables, or independent variables?

The term used for predictors, features, explanatory variables, or independent variables is "Inputs." In the context of predictive modeling and data analysis, inputs refer to the variables that are used to make predictions or inform decisions about a target variable, also known as the dependent variable. Inputs hold the information that allows models to learn patterns and ultimately derive outcomes based on the relationships found within the data. This is crucial for building effective models, as the choice of inputs can significantly influence the model's performance.

Choosing the appropriate inputs is essential in any analytical process, as they serve as the foundation upon which predictions are made. In contrast, other terms listed, such as targets, outputs, and sample data, refer to different aspects of the analytical process, emphasizing the importance of clearly distinguishing between these terminologies to avoid confusion. Targets are the outcomes or variables that the model aims to predict, whereas outputs represent the results returned by the model once trained. Sample data refers to the collection of instances used for analysis, not specifically to the characteristics or features driving the model's predictive ability.