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Uncertainty Quantification in Ensembles of Honest Regression Trees using Generalized Fiducial Inference
The main contribution is a new method that quantifies the uncertainties of the estimates and predictions produced by honest random forests.
Measuring the Algorithmic Convergence of Randomized Ensembles: The Regression Setting
When randomized ensemble methods such as bagging and random forests are implemented, a basic question arises: Is the ensemble large enough?
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