# gbm.perf

From gbm v0.6
0th

Percentile

##### 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 http://www.i-pensieri.com/gregr/gbm.shtml.

gbm, gbm.object