A working guide to boosted regression trees.

By Jane Elith, J. Leathwick, Trevor J. Hastie. 2008

6,148 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Ecological Modeling, Nature and Landscape Conservation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Ecological Modeling

In Nature and Landscape Conservation

Citations in the corpus

Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) (2018, background): “[110], who rely on stochastic gradient boosting, used PDPs to understand how different environmental factors influence the distribution of a particular freshwater.”
Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation (2013, methodology): “Another particularly useful model agnostic tool is Friedman (2001)’s PDP, which this article extends.”
On Evaluating and Comparing Conversational Agents (2018, methodology): “We trained a model using Gradient Boosted Tree (GBDT) (Elith et al., 2008) and Hierarchical LSTM (HLSTM) (Serban et al., 2016) to estimate user ratings of conversations.”
A global map of dominant malaria vectors (2012, methodology): “Using the boosted regression tree (BRT) modelling methodology [16], predicted distribution maps were produced for each DVS.”
A review of machine learning applications in wildfire science and management (2020, background): “In a number of domains, and particularly in the context of ecological modeling GBM is often referred to as Boosted Regression Trees (BRTs) [Elith et al., 2008].”
Hierarchical Species Distribution Models (2016, methodology): “Other commonly used methods that rely on basis functions include regression trees [29, 44], some maximum entropy methods (e.”
The dominant Anopheles vectors of human malaria in Africa, Europe and the Middle East: occurrence data, distribution maps and bionomic précis (2010, methodology): “The BRT method [65,66] was chosen to generate the predictive maps of each DVS distribution.”
Relationship Between Soil Properties and Banana Productivity in the Two Main Cultivation Areas in Venezuela (2020, methodology): “This relative importance (or contribution) of each variable is scaled so that the sum adds up to 100, with higher numbers indicating a greater influence on the response; in this case, a threshold value 3.0% was used (Elith et al. 2008).”
Global hotspots and correlates of emerging zoonotic diseases (2017, methodology): “All replicate BRT models were ﬁ t using the R packages dismo and gbm 26 .”