gbm.holdout: gbm holdout

In dismo: Species Distribution Modeling

gbm holdout

Description

Calculates a gradient boosting (gbm) object in which model complexity is determined using a training set with predictions made to a withheld set. An initial set of trees is fitted, and then trees are progressively added testing performance along the way, using gbm.perf until the optimal number of trees is identified.

As any structured ordering of the data should be avoided, a copy of the data set is BY DEFAULT randomly reordered each time the function is run.

Usage

gbm.holdout(data, gbm.x, gbm.y, learning.rate = 0.001, tree.complexity = 1, 
 family = "bernoulli", n.trees = 200, add.trees = n.trees, max.trees = 20000, 
 verbose = TRUE, train.fraction = 0.8, permute = TRUE, prev.stratify = TRUE,
 var.monotone = rep(0, length(gbm.x)), site.weights = rep(1, nrow(data)), 
 refit = TRUE, keep.data = TRUE)

Arguments

data	data.frame
gbm.x	indices of the predictors in the input dataframe
gbm.y	index of the response in the input dataframe
learning.rate	typically varied between 0.1 and 0.001
tree.complexity	sometimes called interaction depth
family	"bernoulli","poisson", etc. as for gbm
n.trees	initial number of trees
add.trees	number of trees to add at each increment
max.trees	maximum number of trees to fit
verbose	controls degree of screen reporting
train.fraction	proportion of data to use for training
permute	reorder data to start with
prev.stratify	stratify selection for presence/absence data
var.monotone	allows constraining of response to monotone
site.weights	set equal to 1 by default
refit	refit the model with the full data but id'd no of trees
keep.data	keep copy of the data

Value

A gbm object

Author(s)

John R. Leathwick and Jane Elith

References

Elith, J., J.R. Leathwick and T. Hastie, 2009. A working guide to boosted regression trees. Journal of Animal Ecology 77: 802-81

dismo documentation built on May 31, 2023, 6:15 p.m.