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2016

Making Tree Ensembles Interpretable

Hara, Satoshi, Hayashi, Kohei

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Tree ensembles, such as random forest and boosted trees, are renowned for their high prediction performance, whereas their interpretability is critically limited.

In this paper, we propose a post processing method that improves the model interpretability of tree ensembles.
After learning a complex tree ensembles in a standard way, we approximate it by a simpler model that is interpretable for human.
To obtain the simpler model, we derive the EM algorithm minimizing the KL divergence from the complex ensemble.

Making Tree Ensembles Interpretable · Around

Built on

Classification and Regression Trees
Breiman, L., Friedman, J., Stone, C. J., and Olshen, R. A · 1984
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Hierarchical mixtures of experts and the em algorithm
Jordan, M. I. and Jacobs, R. A · 1994
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A system for induction of oblique decision trees
Murthy, S. K., Kasif, S., and Salzberg, S · 1994
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Random forests
Breiman, L · 2001
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Greedy function approximation: A gradient boosting machine
Friedman, J. H · 2001
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