plot_gbm | R Documentation |
Plot a gbm
model showing the training and other
error curves.
plot_gbm(object=stop("no 'object' argument"),
smooth = c(0, 0, 0, 1),
col = c(1, 2, 3, 4), ylim = "auto",
legend.x = NULL, legend.y = NULL, legend.cex = .8,
grid.col = NA,
n.trees = NA, col.n.trees ="darkgray",
...)
object |
The |
smooth |
Four-element vector specifying if smoothing should be applied
to the train, test, CV, and OOB curves respectively.
When smoothing is specified, a smoothed curve is plotted and the
minimum is calculated from the smoothed curve. |
col |
Four-element vector specifying the colors for the train, test, CV, and OOB
curves respectively. |
ylim |
The default |
legend.x |
The x position of the legend.
The default positions the legend automatically. |
legend.y |
The y position of the legend. |
legend.cex |
The legend |
grid.col |
Default |
n.trees |
For use by |
col.n.trees |
For use by |
... |
Dot arguments are passed internally to
|
This function returns a four-element vector specifying the number of trees at the train, test, CV, and OOB minima respectively.
The minima are calculated after smoothing as specified by this
function's smooth
argument.
By default, only the OOB curve is smoothed.
The smoothing algorithm for the OOB curve differs slightly
from gbm.perf
, so can give a slightly
different number of trees.
The OOB curve
The OOB curve is artificially rescaled to force it into the plot. See Chapter 7 in the plotres vignette.
Interaction with plotres
When invoking this function via plotres
, prefix any
argument of plotres
with w1.
to tell plotres
to
pass the argument to this function.
For example give w1.ylim=c(0,10)
to plotres
(plain
ylim=c(0,10)
in this context gets passed to the residual
plots).
Acknowledgments
This function is derived from code in the gbm
package authored by Greg Ridgeway and others.
Chapter 7 in plotres vignette discusses this function.
if (require(gbm)) {
n <- 100 # toy model for quick demo
x1 <- 3 * runif(n)
x2 <- 3 * runif(n)
x3 <- sample(1:4, n, replace=TRUE)
y <- x1 + x2 + x3 + rnorm(n, 0, .3)
data <- data.frame(y=y, x1=x1, x2=x2, x3=x3)
mod <- gbm(y~., data=data, distribution="gaussian",
n.trees=300, shrinkage=.1, interaction.depth=3,
train.fraction=.8, verbose=FALSE)
plot_gbm(mod)
# plotres(mod) # plot residuals
# plotmo(mod) # plot regression surfaces
}
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