gbm.fixed

0th

Percentile

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 1.3-3, License: GPL (>= 3)

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