summary.GBMFit | R Documentation |
Computes the relative influence of each variable in the
GBMFit
object.
## S3 method for class 'GBMFit'
summary(
object,
cBars = length(object$variables$var_names),
num_trees = length(trees(object)),
plot_it = TRUE,
order_it = TRUE,
method = relative_influence,
normalize = TRUE,
...
)
object |
a |
cBars |
the number of bars to plot. If |
num_trees |
the number of trees used to generate the
plot. Only the first |
plot_it |
an indicator as to whether the plot is generated. |
order_it |
an indicator as to whether the plotted and/or returned relative influences are sorted. |
method |
The function used to compute the relative influence.
|
normalize |
if |
... |
other arguments passed to the plot function. |
For GBMGaussianDist
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.
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.
James Hickey, Greg Ridgeway gregridgeway@gmail.com
J.H. Friedman (2001). "Greedy Function Approximation: A Gradient Boosting Machine," Annals of Statistics 29(5):1189-1232.
L. Breiman (2001). Random Forests.
gbmt
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