IREX: Iterative Refinement and Explanation of classification models for tabular datasets

Tabular datasets, collections of rows and columns, are fundamental in data analysis basically all areas research. Self-report questionnaires a very common useful tool for gathering from users, patients, or customers. Often, experts can label each item these as measure given condition behavior, e.g., mental health conditions such depression. Considering this, many artificial intelligence techniques, particularly those related to machine learning, have been proposed analyze the provided by tools. However, self-report be extensive, which often affects quality responses, complicates analysis, renders them more time-consuming. In this paper, software IREX is presented. iteratively refines tabular questionnaires, while providing an explanation classification model.