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arXiv · 1904.05394

Enhancing Decision Tree based Interpretation of Deep Neural Networks through L1-Orthogonal Regularization

Nina Schaaf·Marco F. Huber·Johannes Maucher
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Abstract

One obstacle that so far prevents the introduction of machine learning models primarily in critical areas is the lack of explainability. In this work, a practicable approach of gaining explainability of deep artificial neural networks (NN) using an interpretable surrogate model based on decision trees is presented. Simply fitting a decision tree to a trained NN usually leads to unsatisfactory results in terms of accuracy and fidelity. Using L1-orthogonal regularization during training, however, preserves the accuracy of the NN, while it can be closely approximated by small decision trees. Tests with different data sets confirm that L1-orthogonal regularization yields models of lower complexity and at the same time higher fidelity compared to other regularizers.

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Nina Schaaf, Marco F. Huber, Johannes Maucher. 2019-10-03. Enhancing Decision Tree based Interpretation of Deep Neural Networks through L1-Orthogonal Regularization. https://arxiv.org/abs/1904.05394

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