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Table 1 Settings and explanations of the TreeNet model run

From: Ecological niche modeling of rabies in the changing Arctic of Alaska

Metric Setting Effect Justification
Learnrate AUTO A detailed but slow model run Known to provide best results for the algorithm ‘learning’ data
Subsample fraction 50% Internal testing while model is grown Standard approach for balanced tree models
Logistic residual trim fraction 0.10 Fine-tuning Allows for better fits
Huber-M fraction of error squared 0.90 Accuracy level A statistical standard threshold for certainty
Optimal logistic model selection Cross entropy How to find the optimal model Usually the best setting for tree-based models
Number of trees to build 1000 Number of trees tried out for the best solution This number should widely overshot the known optimum
Maximum number of nodes 6 Determines the node depth of trees used This number determines whether a ‘stump’ or a fully fit tree is run
Terminal node minimum training cases 10 For most data cases it provides a robust tree Number of cases for each tree branch split
Maximum number of most-optimal models to save summary results 1 Just 1 most-optimal model is saved  
Regression loss criterion Huber-M (Blend LS and LAD) A statistical metric to express gain vs cost of a new rule Standard approach in trees