# gbm.perf

##### GBM performance

Estimates the optimal number of boosting iterations for a `gbm`

object
and optionally plots various performance measures

- Keywords
- nonparametric, tree, nonlinear, survival

##### Usage

```
gbm.perf(object, plot.it = TRUE, oobag.curve = FALSE, overlay = TRUE,
method)
```

##### Arguments

- object
A

`gbm.object`

created from an initial call to`gbm`

.- plot.it
An indicator of whether or not to plot the performance measures. Setting

`plot.it = TRUE`

creates two plots. The first plot plots`object$train.error`

(in black) and`object$valid.error`

(in red) versus the iteration number. The scale of the error measurement, shown on the left vertical axis, depends on the`distribution`

argument used in the initial call to`gbm`

.- oobag.curve
Indicates whether to plot the out-of-bag performance measures in a second plot.

- overlay
If TRUE and oobag.curve=TRUE then a right y-axis is added to the training and test error plot and the estimated cumulative improvement in the loss function is plotted versus the iteration number.

- method
Indicate the method used to estimate the optimal number of boosting iterations.

`method = "OOB"`

computes the out-of-bag estimate and`method = "test"`

uses the test (or validation) dataset to compute an out-of-sample estimate.`method = "cv"`

extracts the optimal number of iterations using cross-validation if`gbm`

was called with`cv.folds`

> 1.

##### Value

`gbm.perf`

Returns the estimated optimal number of iterations.
The method of computation depends on the `method`

argument.

##### See Also

*Documentation reproduced from package gbm, version 2.1.5, License: GPL (>= 2) | file LICENSE*