# gbm.perf

From gbm v0.6
by Greg Ridgeway

##### 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 = TRUE,
overlay = TRUE,
best.iter.calc = c("OOB","test")[1])
```

##### 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 nu - 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.
- best.iter.calc
- indicate the method used to estimate the optimal number
of boosting iterations.
`best.iter.calc="OOB"`

computes the out-of-bag estimate and`best.iter.calc="test"`

uses the test (or validation) dataset to compute an out-of-sample

##### Value

`gbm.perf`

returns the estimated optimal number of iterations. The method of computation depends on the`best.iter.calc`

argument.

##### References

G. Ridgeway (2003). "A note on out-of-bag estimation for estimating the optimal
number of boosting iterations," a working paper available at

##### See Also

*Documentation reproduced from package gbm, version 0.6, License: GPL (version 2 or newer)*

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