266 citations · 492 across the 10 of their papers we have counts for
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stat.ML2018★ 22 cited
Making Sense of Random Forest Probabilities: a Kernel Perspective
Matthew A. Olson, Abraham J. Wyner
A random forest is a popular tool for estimating probabilities in machine learning classification tasks. However, the means by which this is accomplished is unprincipled: one simpl…
stat.ML2015★ 61 cited
Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers
Abraham J. Wyner, Matthew Olson, Justin Bleich +1
There is a large literature explaining why AdaBoost is a successful classifier. The literature on AdaBoost focuses on classifier margins and boosting's interpretation as the optimi…