Computes the relative influence of each variable in the gbm object.
# S3 method for gbm
summary(
object,
cBars = length(object$var.names),
n.trees = object$n.trees,
plotit = TRUE,
order = TRUE,
method = relative.influence,
normalize = TRUE,
...
)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.
a gbm object created from an initial call to
gbm.
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.
the number of trees used to generate the plot. Only the first
n.trees trees will be used.
an indicator as to whether the plot is generated.
an indicator as to whether the plotted and/or returned relative influences are sorted.
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).
if FALSE then summary.gbm returns the
unnormalized influence.
other arguments passed to the plot function.
Greg Ridgeway gregridgeway@gmail.com
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.
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.
gbm