3 citations · 7 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
Locally Optimized Random Forests
Tim Coleman, Kimberly Kaufeld, Mary Frances Dorn +1
Standard supervised learning procedures are validated against a test set that is assumed to have come from the same distribution as the training data. However, in many problems, th…
Bootstrap Bias Corrections for Ensemble Methods
Giles Hooker, Lucas Mentch
This paper examines the use of a residual bootstrap for bias correction in machine learning regression methods. Accounting for bias is an important obstacle in recent efforts to de…