gbm.fixed
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