A gbm.object created from an initial call to gbm.

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.

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. 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.