Search Results for author:
Found 3 papers, 2 papers with code
Locally Optimized Random Forests
To estimate these ratios with an unlabeled test set, we make the covariate shift assumption, where the differences in distribution are only a function of the training distributions (Shimodaira, 2000.)
Asymptotic Distributions and Rates of Convergence for Random Forests via Generalized U-statistics
Random forests remain among the most popular off-the-shelf supervised learning algorithms.
Scalable and Efficient Hypothesis Testing with Random Forests
Throughout the last decade, random forests have established themselves as among the most accurate and popular supervised learning methods.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
