gbm.fixed

0th

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gbm fixed

Calculates a gradient boosting (gbm) object with a fixed number of trees. The optimal number of trees can be identified using gbm.step or some other procedure. Mostly used as a utility function, e.g., when being called by gbm.simplify. It takes as input a dataset and arguments selecting x and y variables, learning rate and tree complexity.

Keywords
spatial
Usage
gbm.fixed(data, gbm.x, gbm.y, tree.complexity = 1, site.weights = rep(1, nrow(data)), verbose = TRUE, learning.rate = 0.001, n.trees = 2000, bag.fraction = 0.5, family = "bernoulli", keep.data = FALSE, var.monotone = rep(0, length(gbm.x)))
Arguments
data
data.frame
gbm.x
indices of the predictors in the input dataframe
gbm.y
index of the response in the input dataframe
tree.complexity
the tree depth - sometimes referred to as interaction depth
site.weights
by default set equal
verbose
to control reporting
learning.rate
controls speed of the gradient descent
n.trees
default number of trees
bag.fraction
varies random sample size for each new tree
family
can be any of "bernoulli", "poisson", "gaussian", or "laplace"
keep.data
Logical. If TRUE, original data is kept
var.monotone
constrain to positive (1) or negative monontone (-1)
Value

object of class gbm

References

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

Aliases
  • gbm.fixed
Documentation reproduced from package dismo, version 1.1-4, License: GPL (>= 3)

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