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
    )

    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.

### 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). https://www.stat.berkeley.edu/users/breiman/randomforest2001.pdf.

### See Also

`gbm`

[Package gbm version 2.1.8 Index]