Trading-Off Interpretability and Accuracy in Medical Applications: A Study Toward Optimal Explainability of Hoeffding Trees

With an increased number of applications of machine learning models to support decision-making in critical domains, there is a pressing need to understand the internal behavior of these models. Essentially, explaining learning models to humans has expedited the development of methods to extract information and access models' inner components. Researchers have proposed approaches to compute explanations for different types of models. While these approaches are often effective in explaining complex relations and rough input-output maps, studies on model optimality, which trade off accuracy and interpretability are scarce. We conducted a study to understand the relationship between accuracy and interpretability of Hoeffding trees, developed from evolving data streams. We employed formal reasoning techniques, founded on theoretical guarantees, to generate subset-minimal explanations for a set of examples. Rankings of features, according to their importance to the model outcomes, were obtained. After computing model accuracy and interpretability, the least important feature based on the ranking was removed. By repeating this procedure, we leveraged the setup that leads to an optimal (accurate and interpretable) tree model. Application examples considered medical datasets, namely the Parkinson's Disease telemonitoring and the EEG eye-state datasets, to generate Hoeffding regression and classification trees, respectively. The study has shown that as tree interpretability increases, accuracy tends to decrease; however, an optimal solution can be established by balancing conflicting aspects.