Multiple additive regression trees with application in epidemiology

By Jerome H. Friedman, Jacqueline J. Meulman. Statistics in Medicine, 2003

768 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c1eb920ae8d8557dc744481147534892bb91e3e3

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Citations in the corpus

Understanding what controls the spatial distribution of fish populations using a multi-model approach (2011, background): “…2003, Joy and Death, 2004, Wieland and JarreTeichmann, 1997), regression trees (De'ath and Fabricius, 2000, De'ath, 2002, Sutton et al., 2005, Stratoudakis et al., 1998, Usio, 2007) and their boosted form (Friedman et al., 2000, Friedman and Meulma, 2003, Leathwick et al., 2006, Friedman, 2001).”
Error and uncertainty in habitat models (2006, methodology): “Boosted trees and other emerging methods are able to fit complex response surfaces and have improved ability to fit interactions in an efficient manner ( Friedman & Meulman 2003 ).”
Environmental influences on the occurrence of coastal sharks in estuarine waters (2010, background): “The relative importance of variables can be determined by averaging the number of times a variable is selected for splitting and the squared improvement resulting from these splits (Friedman 2001, Friedman & Meulman 2003).”
GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran (2015, background): “Furthermore, advantages of BRT, CART, and RF include their (i) ability to accommodate different types of predictor variables and missing values and (ii) ability to facilitate for Bfitting^ interactions between predictors (Friedman and Meulman 2003).”
Principles and Practice of Explainable Machine Learning (2020, neutral): “It is also worth noting that, as with linear regression, visualization tools are often employed to communicate the results of the analysis (such as partial dependence plots [29]).”
The global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus (2015, neutral): “…(0–100) on explaining the variance in the models calculated as the sum of the number of times a particular variable is selected for splitting the decision tree, weighted by the squared improvement to the overall model averaged over all trees ( Friedman, 2001 ; Friedman and Meulman, 2003 ).”
Coral reef benthic regimes exhibit non-linear threshold responses to natural physical drivers (2015, methodology): “The relative importance of each predictor variable was quantified based on the number of times it was selected for splitting, weighted by the squared improvement to the model as a result of each split and averaged over all trees (Friedman & Meulman 2003, Elith et al. 2008).”
Comparing the determinants of cropland abandonment in Albania and Romania using boosted regression trees (2013, methodology): “The relative importance is derived based on the number of times a variable is selected for splitting, weighted by the squared improvements, and averaged over all of the trees (Friedman and Meulman, 2003).”
Extrapolating regional soil landscapes from an existing soil map: Sampling intensity, validation procedures, and integration of spatial context (2008, background): “Recent studies identified advantages of using boosted trees compared with simple trees: improvement of accuracy (Moran and Bui, 2002; Lawrence et al., 2004), little tuning needed, and high robustness (Friedman and Meulman, 2003).”
Forest productivity increases with evenness, species richness and trait variation: a global meta‐analysis (2012, background): “Journal of Ecology 2012 British Ecological Society, Journal of Ecology, 100, 742–749 average of all trees in a BRT model (Friedman & Meulman 2003).”
Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods (2019, methodology): “However, variations such as the Gradient boosting machine is used for both classification and regression tasks.(40,41) Gradient boosting machine is an ensemble method that uses weak predictors (eg, decision trees) that can ultimately be boosted and lead to a better performing model (ie, the boosted ”
A working guide to boosted regression trees. (2008, methodology): “While these graphs are not a perfect representation of the effects of each variable, particularly if there are strong interactions in the data or predictors are strongly correlated, they provide a useful basis for interpretation (Friedman 2001; Friedman & Meulman 2003).”