Researchers at Duke University recently formulated an approach to check the importance of variables for various, almost-optimal predictive models. The researchers, Jiayun Dong and Cynthia Rudin, refer to this approach as “variable importance clouds.” Their approach could ultimately help develop more reliable and better-performing machine learning algorithms for various applications and to enhance predictive models’ reliability/accuracy.
The idea behind the given term is to take multiple models (i.e., a whole “cloud” of these models) that one can assess in terms of variable importance. The clouds can help researchers to identify variables based on their importance. Typically, the importance of one variable suggests that another variable is less important for a given model’s predictions. The “cloud” is the set of models seen through the lens of variable importance.