2 papers
stat.ML2019
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…
stat.ME2019
Scalable and Efficient Hypothesis Testing with Random Forests
Tim Coleman, Wei Peng, Lucas Mentch
Throughout the last decade, random forests have established themselves as among the most accurate and popular supervised learning methods. While their black-box nature has made the…