Estimates the optimal number of boosting iterations for a
gbm object and
optionally plots various performance measures
gbm.perf(object, plot.it = TRUE, oobag.curve = TRUE, overlay = TRUE, best.iter.calc = c("OOB","test"))
gbm.objectcreated from an initial call to
- an indicator of whether or not to plot the performance measures.
plot.it=TRUEcreates two plots. The first plot plots
object$train.error(in black) and
object$valid.error(in red) versus the iteration nu
- indicates whether to plot the out-of-bag performance measures in a second plot.
- 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.
- 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
gbm.perfreturns the estimated optimal number of iterations. The method of computation depends on the
G. Ridgeway (2003). "A note on out-of-bag estimation for estimating the optimal
number of boosting iterations," a working paper available at