184 citations · 577 across the 31 of their papers we have counts for
Showing 2016 · stat.MLShow all
3 papers · 2 filters
stat.ML2016★ 4 cited
Finding Alternate Features in Lasso
Satoshi Hara, Takanori Maehara
We propose a method for finding alternate features missing in the Lasso optimal solution. In ordinary Lasso problem, one global optimum is obtained and the resulting features are i…
stat.ML2016★ 60 cited
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★ 18 cited
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…