Prints some information about the model object. In particular, this method
prints the call to gbm(), the type of loss function that was used,
and the total number of iterations.
If cross-validation was performed, the 'best' number of trees as estimated
by cross-validation error is displayed. If a test set was used, the 'best'
number of trees as estimated by the test set error is displayed.
The number of available predictors, and the number of those having non-zero
influence on predictions is given (which might be interesting in data mining
applications).
If multinomial, bernoulli or adaboost was used, the confusion matrix and
prediction accuracy are printed (objects being allocated to the class with
highest probability for multinomial and bernoulli). These classifications
are performed on the entire training data using the model with the 'best'
number of trees as described above, or the maximum number of trees if the
'best' cannot be computed.
If the 'distribution' was specified as gaussian, laplace, quantile or
t-distribution, a summary of the residuals is displayed. The residuals are
for the training data with the model at the 'best' number of trees, as
described above, or the maximum number of trees if the 'best' cannot be
computed.