We present an approach for the detection of feature interactions. It is based on two concepts: Call-Context Model (CCM) and Feature Context Assumptions (FCA). CCM specifies the possible call-connection(s) related to the feature and FCA summarizes the “important” assumptions about the feature environment. By analyzing the CCM-FCAs of two or more given features, we determine the existence or abse...

Feature selection is an essential step in data science pipelines to reduce the complexity associated with large datasets. While much research on this topic focuses optimizing predictive performance, few studies investigate stability context of feature process. In study, we present Repeated Elastic Net Technique (RENT) for Selection. RENT uses ensemble generalized linear models elastic net regul...