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