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Instead of relying on a single decision tree, using the majority vote of a forest of decision trees fit to bootstrap samples from the original data.
To estimate the mean, median and 95% range of δh and δm, we obtained 10,000 replications of the mean of bootstrap samples from the measurements at the given sample sizes using mean(sample x, length(x), replace = TRUE)) in R 2.14.1.
We took one thousand bootstrap samples from the original data.
Extract bootstrap samples from the donor and recipient files.
We generated 1,000 bootstrap samples from the original data set.
With this approach, we selected 1000 replicated bootstrap samples from the original cohort.
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It is estimated internally considering that each tree is constructed using a different bootstrap sample from the original data.
For each RBI we separately calculated the 5% confidence limits using bootstrap sampling from the set of events.
The Random Forests consist of many decision trees and each tree is constructed by a bootstrap sample from the original data.
In a Random Forests ensemble, each tree is built on a bootstrap sample from the original learning sample and, at each test node, K attributes are selected at random among all candidate attributes before determining the best split.
Generation of a bootstrap sample from the relative sample comprising n r subjects.
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