Function to assess the optimal number of boosting trees using k-fold cross validation. This is an implementation of the cross-validation procedure described on page 215 of Hastie et al (2001).
The data is divided into 10 subsets, with stratification by prevalence if required for presence/absence data. The function then fits a gbm model of increasing complexity along the sequence from n.trees to n.trees + (n.steps * step.size), calculating the residual deviance at each step along the way. After each fold processed, the function calculates the average holdout residual deviance and its standard error and then identifies the optimal number of trees as that at which the holdout deviance is minimised. It fits a model with this number of trees, returning it as a gbm model along with additional information from the cross-validation selection process.
gbm.step(data, gbm.x, gbm.y, offset = NULL, fold.vector = NULL, tree.complexity = 1,
learning.rate = 0.01, bag.fraction = 0.75, site.weights = rep(1, nrow(data)),
var.monotone = rep(0, length(gbm.x)), n.folds = 10, prev.stratify = TRUE,
family = "bernoulli", n.trees = 50, step.size = n.trees, max.trees = 10000,
tolerance.method = "auto", tolerance = 0.001, plot.main = TRUE, plot.folds = FALSE,
verbose = TRUE, silent = FALSE, keep.fold.models = FALSE, keep.fold.vector = FALSE,
keep.fold.fit = FALSE, ...)object of S3 class gbm
input data.frame
indices or names of predictor variables in data
index or name of response variable in data
offset
a fold vector to be read in for cross validation with offsets
sets the complexity of individual trees
sets the weight applied to inidivudal trees
sets the proportion of observations used in selecting variables
allows varying weighting for sites
restricts responses to individual predictors to monotone
number of folds
prevalence stratify the folds - only for presence/absence data
family - bernoulli (=binomial), poisson, laplace or gaussian
number of initial trees to fit
numbers of trees to add at each cycle
max number of trees to fit before stopping
method to use in deciding to stop - "fixed" or "auto"
tolerance value to use - if method == fixed is absolute, if auto is multiplier * total mean deviance
Logical. plot hold-out deviance curve
Logical. plot the individual folds as well
Logical. control amount of screen reporting
Logical. to allow running with no output for simplifying model)
Logical. keep the fold models from cross valiation
Logical. allows the vector defining fold membership to be kept
Logical. allows the predicted values for observations from cross-validation to be kept
Logical. allows for any additional plotting parameters
John R. Leathwick and Jane Elith
Hastie, T., R. Tibshirani, and J.H. Friedman, 2001. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer-Verlag, New York Elith, J., J.R. Leathwick and T. Hastie, 2009. A working guide to boosted regression trees. Journal of Animal Ecology 77: 802-81
data(Anguilla_train)
# reduce data set to speed things up a bit
Anguilla_train = Anguilla_train[1:200,]
angaus.tc5.lr01 <- gbm.step(data=Anguilla_train, gbm.x = 3:14, gbm.y = 2, family = "bernoulli",
tree.complexity = 5, learning.rate = 0.01, bag.fraction = 0.5)
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