R: Cross-validate a gbm

gbmCrossVal {gbm} | R Documentation

## 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.

### Details

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

### Value

A list containing the cross-validation error and predictions.

### Author(s)

Greg Ridgeway gregridgeway@gmail.com

### 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`

[Package gbm version 2.1.3 Index]