Summary of a gbm object

Computes the relative influence of each variable in the gbm object.

Usage

# S3 method for class 'gbm'
summary(
  object,
  cBars = length(object$var.names),
  n.trees = object$n.trees,
  plotit = TRUE,
  order = TRUE,
  method = relative.influence,
  normalize = TRUE,
  ...
)

Arguments

object: a gbm object created from an initial call to gbm.
cBars: the number of bars to plot. If order=TRUE the only the variables with the cBars largest relative influence will appear in the barplot. If order=FALSE then the first cBars variables will appear in the plot. In either case, the function will return the relative influence of all of the variables.
n.trees: the number of trees used to generate the plot. Only the first n.trees trees will be used.
plotit: an indicator as to whether the plot is generated.
order: an indicator as to whether the plotted and/or returned relative influences are sorted.
method: The function used to compute the relative influence. relative.influence is the default and is the same as that described in Friedman (2001). The other current (and experimental) choice is permutation.test.gbm. This method randomly permutes each predictor variable at a time and computes the associated reduction in predictive performance. This is similar to the variable importance measures Breiman uses for random forests, but gbm currently computes using the entire training dataset (not the out-of-bag observations).
normalize: if FALSE then summary.gbm returns the unnormalized influence.
...: other arguments passed to the plot function.

Value

Returns a data frame where the first component is the variable name and the second is the computed relative influence, normalized to sum to 100.

Details

For distribution="gaussian" this returns exactly the reduction of squared error attributable to each variable. For other loss functions this returns the reduction attributable to each variable in sum of squared error in predicting the gradient on each iteration. It describes the relative influence of each variable in reducing the loss function. See the references below for exact details on the computation.

References

J.H. Friedman (2001). "Greedy Function Approximation: A Gradient Boosting Machine," Annals of Statistics 29(5):1189-1232.

L. Breiman (2001).https://www.stat.berkeley.edu/users/breiman/randomforest2001.pdf.

Author

Greg Ridgeway gregridgeway@gmail.com