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arXiv · 1906.01297

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

Xavier Renard·Nicolas Woloszko·Jonathan Aigrain·Marcin Detyniecki
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Abstract

Interpretable surrogates of black-box predictors trained on high-dimensional tabular datasets can struggle to generate comprehensible explanations in the presence of correlated variables. We propose a model-agnostic interpretable surrogate that provides global and local explanations of black-box classifiers to address this issue. We introduce the idea of concepts as intuitive groupings of variables that are either defined by a domain expert or automatically discovered using correlation coefficients. Concepts are embedded in a surrogate decision tree to enhance its comprehensibility. First experiments on FRED-MD, a macroeconomic database with 134 variables, show improvement in human-interpretability while accuracy and fidelity of the surrogate model are preserved.

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Xavier Renard, Nicolas Woloszko, Jonathan Aigrain, Marcin Detyniecki. 2019-06-04. Concept Tree: High-Level Representation of Variables for More Interpretable Surrogate Decision Trees. https://arxiv.org/abs/1906.01297

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