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

Understand

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.