Explaining random forest prediction through diverse rulesets
- URL: http://arxiv.org/abs/2203.15511v1
- Date: Tue, 29 Mar 2022 12:54:57 GMT
- Title: Explaining random forest prediction through diverse rulesets
- Authors: Klest Dedja, Felipe Kenji Nakano, Konstantinos Pliakos, Celine Vens
- Abstract summary: Local Tree eXtractor (LTreeX) is able to explain the forest prediction for a given test instance with a few diverse rules.
We show that our proposed approach substantially outperforms other explainable methods in terms of predictive performance.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Tree-ensemble algorithms, such as random forest, are effective machine
learning methods popular for their flexibility, high performance, and
robustness to overfitting. However, since multiple learners are combined,they
are not as interpretable as a single decision tree. In this work we propose a
methodology, called Local Tree eXtractor (LTreeX) which is able to explain the
forest prediction for a given test instance with a few diverse rules. Starting
from the decision trees generated by a random forest, our method 1) pre-selects
a subset of them, 2) creates a vector representation, and 3) eventually
clusters such a representation. Each cluster prototype results in a rule that
explains the test instance prediction. We test the effectiveness of LTreeX on
71 real-world datasets and we demonstrate the validity of our approach for
binary classification, regression, multi-label classification and time-to-event
tasks. In all set-ups, we show that our extracted surrogate model manages to
approximate the performance of the corresponding ensemble model, while
selecting only few trees from the whole forest.We also show that our proposed
approach substantially outperforms other explainable methods in terms of
predictive performance.
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