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.