4 papers · 1 filter
Bayesian Neural Networks for Functional ANOVA model
Seokhun Park, Choeun Kim, Jihu Lee +3
With the increasing demand for interpretability in machine learning, functional ANOVA decomposition has gained renewed attention as a principled tool for breaking down high-dimensi…
Bayesian Additive Regression Trees for functional ANOVA model
Seokhun Park, Insung Kong, Yongdai Kim
Bayesian Additive Regression Trees (BART) is a powerful statistical model that leverages the strengths of Bayesian inference and regression trees. It has received significant atten…
Tensor Product Neural Networks for Functional ANOVA Model
Seokhun Park, Insung Kong, Yongchan Choi +2
Interpretability for machine learning models is becoming more and more important as machine learning models become more complex. The functional ANOVA model, which decomposes a high…
META-ANOVA: Screening interactions for interpretable machine learning
Yongchan Choi, Seokhun Park, Chanmoo Park +2
There are two things to be considered when we evaluate predictive models. One is prediction accuracy,and the other is interpretability. Over the recent decades, many prediction mod…