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Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees.
High performance parallel implementation (C++/MPI) of gradient boosted regression trees.
Buston, P. M. & Elith, J. Determinants of reproductive success in dominant pairs of clownfish: a boosted regression tree analysis.
Boosted regression trees (BRT) were used to model the presence-absence of human orthohantavirus disease cases67 ('gbm' package in R).
Soykan, C. U., Eguchi, T., Kohin, S. & Dewar, H. Prediction of fishing effort distributions using boosted regression trees.
A generalized linear model, generalized additive model, maximum entropy and boosted regression tree methods were used in the analyses.
Boosted regression trees (BRT) were then used to calibrate VNIR spectra to soil properties for a representative subsample.
Using boosted regression trees we disentangled the role of anthropogenic factors, physiography, weather and vegetation on fire activity.
Stochastic gradient boosted regression trees (BRT) were employed for model construction, with a variety of calibration and validation schemes tested.
The spatial distributions of six fishes with contrasting biogeographies were modeled using boosted regression trees and multiscale landscape data.
Physiographic modeling of frost occurrence was then conducted comparing multiple regression (MR) and boosted regression trees (BRT).
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