3 citations · 3 across the 1 of their papers we have counts for
3 papers
stat.ML2021★ 3 cited
Trees, Forests, Chickens, and Eggs: When and Why to Prune Trees in a Random Forest
Siyu Zhou, Lucas Mentch
Due to their long-standing reputation as excellent off-the-shelf predictors, random forests continue remain a go-to model of choice for applied statisticians and data scientists. D…
stat.ML2020
Getting Better from Worse: Augmented Bagging and a Cautionary Tale of Variable Importance
Lucas Mentch, Siyu Zhou
As the size, complexity, and availability of data continues to grow, scientists are increasingly relying upon black-box learning algorithms that can often provide accurate predicti…
stat.ML2019
Randomization as Regularization: A Degrees of Freedom Explanation for Random Forest Success
Lucas Mentch, Siyu Zhou
Random forests remain among the most popular off-the-shelf supervised machine learning tools with a well-established track record of predictive accuracy in both regression and clas…