23 citations · 54 across the 7 of their papers we have counts for
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stat.ML2016
Making Tree Ensembles Interpretable
Satoshi Hara, Kohei Hayashi
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…
stat.ML2016
Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach
Satoshi Hara, Kohei Hayashi
Tree ensembles, such as random forests and boosted trees, are renowned for their high prediction performance. However, their interpretability is critically limited due to the enorm…