plot.GBMFit | R Documentation |
Plots the marginal effect of the selected variables by "integrating" out the other variables.
## S3 method for class 'GBMFit'
plot(
x,
var_index = 1,
num_trees = gbm_fit_obj$params$num_trees,
continuous_resolution = 100,
grid_levels = NULL,
return_grid = FALSE,
type = "link",
...
)
x |
a |
var_index |
a vector of indices or the names of the variables
to plot. If using indices, the variables are indexed in the same
order that they appear in the initial |
num_trees |
the number of trees used to generate the
plot. Only the first |
continuous_resolution |
The number of equally space points at which to evaluate continuous predictors |
grid_levels |
A list containing the points at which to
evaluate each predictor in |
return_grid |
if |
type |
the type of prediction to plot on the vertical
axis. See |
... |
other arguments passed to the plot function |
plot.GBMFit
produces low dimensional projections of the
GBMFit
object, see gbmt
, by integrating out
the variables not included in the var_index
argument. The
function selects a grid of points and uses the weighted tree
traversal method described in Friedman (2001) to do the
integration. Based on the variable types included in the
projection, plot_gbmt
selects an appropriate display
choosing amongst line plots, contour plots, and
lattice
plots. If the default graphics are
not sufficient the user may set return.grid=TRUE
, store the
result of the function, and develop another graphic display more
appropriate to the particular example.
Nothing unless return_grid
is true then
plot.GBMFit
produces no graphics and only returns the grid of
evaluation points and their average predictions.
J.H. Friedman (2001). "Greedy Function Approximation: A Gradient Boosting Machine," Annals of Statistics 29(4).
gbmt
, plot
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