1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2025★ 1 cited
The Impact of Bootstrap Sampling Rate on Random Forest Performance in Regression Tasks
Michał Iwaniuk, Mateusz Jarosz, Bartłomiej Borycki +4
Random Forests (RFs) typically train each tree on a bootstrap sample of the same size as the training set, i.e., bootstrap rate (BR) equals 1.0. We systematically examine how varyi…
cs.LG2024
Bootstrap Sampling Rate Greater than 1.0 May Improve Random Forest Performance
Stanisław Kaźmierczak, Jacek Mańdziuk
Random forests (RFs) utilize bootstrap sampling to generate individual training sets for each component tree by sampling with replacement, with the sample size typically equal to t…