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• W2950657827 abstract "Methods based on Bayesian decision tree ensembles have proven valuable in constructing high-quality predictions, and are particularly attractive in certain settings because they encourage low-order interaction effects. Despite adapting to the presence of low-order interactions for prediction purpose, we show that Bayesian decision tree ensembles are generally anti-conservative for the purpose of conducting interaction detection. We address this problem by introducing Dirichlet process forests (DP-Forests), which leverage the presence of low-order interactions by clustering the trees so that trees within the same cluster focus on detecting a specific interaction. We show on both simulated and benchmark data that DP-Forests perform well relative to existing interaction detection techniques for detecting low-order interactions, attaining very low false-positive and false-negative rates while maintaining the same performance for prediction using a comparable computational budget." @default.
• W2950657827 created "2019-06-27" @default.
• W2950657827 creator A5010332378 @default.
• W2950657827 creator A5039861220 @default.
• W2950657827 date "2018-09-23" @default.
• W2950657827 modified "2023-09-27" @default.
• W2950657827 title "Interaction Detection with Bayesian Decision Tree Ensembles" @default.
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• W2950657827 hasPublicationYear "2018" @default.
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