Description: Explanatory variables are those used in statistical models and machine learning to explain variations in a dependent variable. These variables, also known as independent or predictor variables, are fundamental for understanding the relationship between different factors and the outcome being analyzed. In the context of automated machine learning and explainable artificial intelligence, explanatory variables enable models not only to make predictions but also to provide interpretations of how and why those results occur. The proper selection of these variables is crucial, as they directly influence the accuracy and interpretability of the model. Furthermore, in the field of explainable artificial intelligence, there is a focus on ensuring that the decisions made by models are understandable to users, which means that explanatory variables must be selected and presented in a way that allows tracing their impact on predictions. In summary, explanatory variables are essential for building robust and transparent models that not only predict but also explain data behavior.
