gbm (version 2.1.1)

gbmCrossVal: Cross-validate a gbm

Description

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)
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)
gbmCrossValErr(cv.models, cv.folds, cv.group, nTrain, n.trees)
gbmCrossValPredictions(cv.models, cv.folds, cv.group,
best.iter.cv, distribution, data, y)

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.

i.train

Items in the training set.

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.

X

Index (cross-validation fold) on which to subset.

s

Random seed.

Value

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). Random Forests.

See Also

gbm