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Interpretable surrogates of black-box predictors trained on high-dimensionaltabular datasets can struggle to generate comprehensible explanations in thepresence of correlated variables. We propose a model-agnostic interpretablesurrogate that provides global and local explanations of black-box classifiersto address this issue. We introduce the idea of concepts as intuitive groupingsof variables that are either defined by a domain expert or automaticallydiscovered using correlation coefficients. Concepts are embedded in a surrogatedecision tree to enhance its comprehensibility. First experiments on FRED-MD, amacroeconomic database with 134 variables, show improvement inhuman-interpretability while accuracy and fidelity of the surrogate model arepreserved.

Concept Tree: High-Level Representation of Variables for More Interpretable Surrogate Decision Trees