Display basic information about a gbm object.
# S3 method for gbm print(x, ...) show.gbm(x, ...)
| x | an object of class |
|---|---|
| ... | arguments passed to |
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.
data(iris) iris.mod <- gbm(Species ~ ., distribution="multinomial", data=iris, n.trees=2000, shrinkage=0.01, cv.folds=5, verbose=FALSE, n.cores=1) iris.mod#> gbm(formula = Species ~ ., distribution = "multinomial", data = iris, #> n.trees = 2000, shrinkage = 0.01, cv.folds = 5, verbose = FALSE, #> n.cores = 1) #> A gradient boosted model with multinomial loss function. #> 2000 iterations were performed. #> The best cross-validation iteration was 285. #> There were 4 predictors of which 4 had non-zero influence.#data(lung) #lung.mod <- gbm(Surv(time, status) ~ ., distribution="coxph", data=lung, # n.trees=2000, shrinkage=0.01, cv.folds=5,verbose =FALSE) #lung.mod