Assessing the Degree of Feature Interactions that Determine a Model Prediction

Machine Learning (ML) models rely on capturing important feature interactions to generate predictions. This study is focused on validating the hypothesis that model predictions often depend on interactions involving only a few features. This hypothesis is inspired by t-way combinatorial testing for software systems. In our study, we utilize the notion of Shapley Additive Explanations (SHAP) values to quantify each feature’s contribution to model prediction. We then use a greedy approach to identify a minimal subset of features (t) required to determine a model prediction. Our empirical evaluation is performed on three datasets: Adult Income, Mushroom, and Breast Cancer, and three classification models: Logistic Regression, XGBoost, and SVM. Through our experiments, we find that the majority of predictions are determined by interactions involving only a subset of features.