Cross-validate a gbm

Functions for cross-validating gbm. These functions are used internally and are not intended for end-user direct 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, best.iter.cv,
  distribution, data, y)

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.
best.iter.cv	The iteration with lowest cross-validation error.
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.

See also

gbm