Range Analysis and Applications to Root Causing

Abstract

We propose a supervised learning algorithm whose aim is to derive features that explain the response variable better than the original features. Moreover, when there is a meaning for positive vs negative samples, our aim is to derive features that explain the positive samples, or subsets of positive samples that have the same root-cause. Each derived feature represents a single or multi-dimensional subspace of the feature space, where each dimension is specified as a feature-range pair for numeric features, and as a feature-level pair for categorical features. Unlike most Rule Learning and Subgroup Discovery algorithms, the response variable can be numeric, and our algorithm does not require a discretization of the response. The algorithm has been applied successfully to numerous real-life root-causing tasks in chip design, manufacturing, and validation, at Intel.