Cross-validate a gbm

Functions for cross-validating gbm. These functions are used internally and are not intended for end-user direct usage.

Usage

gbmCrossVal(
  cv.folds,
  nTrain,
  n.cores,
  class.stratify.cv,
  data,
  x,
  y,
  offset,
  distribution,
  w,
  var.monotone,
  n.trees,
  interaction.depth,
  n.minobsinnode,
  shrinkage,
  bag.fraction,
  var.names,
  response.name,
  group
)

gbmCrossValErr(cv.models, cv.folds, cv.group, nTrain, n.trees)

gbmCrossValPredictions(cv.models, cv.folds, cv.group, n.trees.predict, data)

gbmCrossValModelBuild(
  cv.folds,
  cv.group,
  n.cores,
  i.train,
  x,
  y,
  offset,
  distribution,
  w,
  var.monotone,
  n.trees,
  interaction.depth,
  n.minobsinnode,
  shrinkage,
  bag.fraction,
  var.names,
  response.name,
  group
)

gbmDoFold(
  X,
  i.train,
  x,
  y,
  offset,
  distribution,
  w,
  var.monotone,
  n.trees,
  interaction.depth,
  n.minobsinnode,
  shrinkage,
  bag.fraction,
  cv.group,
  var.names,
  response.name,
  group,
  s
)

Arguments

cv.folds: The number of cross-validation folds.
nTrain: The number of training samples.
n.cores: The number of cores to use.
class.stratify.cv: Whether or not stratified cross-validation samples are used.
data: The data.
x: The model matrix.
y: The response variable.
offset: The offset.
distribution: The type of loss function. See gbm.
w: Observation weights.
var.monotone: See gbm.
n.trees: The number of trees to fit.
interaction.depth: The degree of allowed interactions. See gbm.
n.minobsinnode: See gbm.
shrinkage: See gbm.
bag.fraction: See gbm.
var.names: See gbm.
response.name: See gbm.
group: Used when distribution = "pairwise". See gbm.
cv.models: A list containing the models for each fold.
cv.group: A vector indicating the cross-validation fold for each member of the training set.
n.trees.predict: The number of trees to use for cross-validation predictions.
i.train: Items in the training set.
X: Index (cross-validation fold) on which to subset.
s: Random seed.

Value

A list containing the cross-validation error and predictions.

Details

These functions are not intended for end-user direct usage, but are used internally by gbm.

References

J.H. Friedman (2001). "Greedy Function Approximation: A Gradient Boosting Machine," Annals of Statistics 29(5):1189-1232.

L. Breiman (2001). https://www.stat.berkeley.edu/users/breiman/randomforest2001.pdf.

Author

Greg Ridgeway gregridgeway@gmail.com