print.gbm: Print model summary
In DexGroves/gbm-lrd: Generalized Boosted Regression Models

Description Usage Arguments Details Author(s) References See Also Examples

Display basic information about a gbm object.

1 2	## S3 method for class 'gbm' print(x, ...)

`x`	an object of class `gbm`.
`...`	arguments passed to `print.default`.

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 using the cross-validation fitted values.

If the 'distribution' was specified as gaussian, laplace, quantile or t-distribution, a summary of the residuals is displayed. The residuals are the cross-validation residuals. Also, a pseudo R-squared value is displayed. For Gaussian response, this is 1 - sum(r*r) / sum(z*z) where z = y - mean(y). For the other distributions, this is 1 - (median(abs(r)) / mad(y))^2, following the suggestion of Rousseeuw and Leroy (equation 3.11). Note that this definition of a robust R-squared is contentious.

Harry Southworth, Daniel Edwards

P. J. Rousseeuw and A. M. Leroy, Robust Regression and Outlier Detection, Wiley, 1987 (2003).

gbm

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
#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