Search Results for author:
Found 5 papers, 2 papers with code
Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers
We introduce a novel perspective on AdaBoost and random forests that proposes that the two algorithms work for similar reasons.
Inference for the Effectiveness of Personalized Medicine with Software
We demonstrate our method's promise on simulated data as well as on data from a randomized trial investigating two treatments for depression.
Methodology
bartMachine: Machine Learning with Bayesian Additive Regression Trees
We present a new package in R implementing Bayesian additive regression trees (BART).
Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation
This article presents Individual Conditional Expectation (ICE) plots, a tool for visualizing the model estimated by any supervised learning algorithm.
Applications
Prediction with Missing Data via Bayesian Additive Regression Trees
We present a method for incorporating missing data in non-parametric statistical learning without the need for imputation.
