| tune {randomForestSRC} | R Documentation |
Finds the optimal mtry and nodesize tuning parameter for a random forest using out-of-bag (OOB) error. Applies to all families.
## S3 method for class 'rfsrc' tune(formula, data, mtryStart = ncol(data) / 2, nodesizeTry = c(1:9, seq(10, 100, by = 5)), ntreeTry = 50, stepFactor = 1.25, improve = 1e-3, strikeout = 3, maxIter = 25, trace = FALSE, doBest = TRUE, ...)
formula |
A symbolic description of the model to be fit. |
data |
Data frame containing the y-outcome and x-variables. |
mtryStart |
Starting value of mtry. |
nodesizeTry |
Values of nodesize optimized over. |
ntreeTry |
Number of trees used for the tuning step. |
stepFactor |
At each iteration, mtry is inflated (or deflated) by this value. |
improve |
The (relative) improvement in OOB error must be by this much for the search to continue. |
strikeout |
The search is discontinued when the relative
improvement in OOB error is negative. However |
maxIter |
The maximum number of iterations allowed for each mtry bisection search. |
trace |
Print the progress of the search? |
doBest |
Return a forest fit with the optimal mtry and nodesize parameters? |
... |
Further options to be passed to |
Returns a matrix whose first and second columns contain the nodesize and mtry values searched and whose third column is the corresponding OOB error. Uses standardized OOB error and in the case of multivariate forests it is the averaged standardized OOB error over the outcomes and for competing risks it is the averaged standardized OOB error over the event types.
If doBest=TRUE, also returns a forest object fit using the
optimal mtry and nodesize values.
All calculations (including the final optimized forest) are based on
the fast forest interface rfsrcFast.
Using rfsrcFast allows the optimization strategy to be
implemented quickly, however the solution can only be considered
approximate. Users may wish to tweak various options to improve
stability. For example, increasing ntreeTry (which is set to
50 for speed) may help. It is also useful to look at contour plots of
the OOB error as a function of mtry and nodesize (see
example below) to identify regions of the parameter space where error
rate is small.
Hemant Ishwaran and Udaya B. Kogalur
## ------------------------------------------------------------
## White wine classification example
## ------------------------------------------------------------
## load the data
data(wine, package = "randomForestSRC")
wine$quality <- factor(wine$quality)
## default tuning call
o <- tune(quality ~ ., wine)
## here is the optimized forest
print(o$rf)
## visualize the nodesize/mtry OOB surface
if (library("akima", logical.return = TRUE)) {
## nice little wrapper for plotting results
plot.tune <- function(o, linear = TRUE) {
x <- o$results[,1]
y <- o$results[,2]
z <- o$results[,3]
so <- interp(x=x, y=y, z=z, linear = linear)
idx <- which.min(z)
x0 <- x[idx]
y0 <- y[idx]
filled.contour(x = so$x,
y = so$y,
z = so$z,
xlim = range(so$x, finite = TRUE) + c(-2, 2),
ylim = range(so$y, finite = TRUE) + c(-2, 2),
color.palette =
colorRampPalette(c("yellow", "red")),
xlab = "nodesize",
ylab = "mtry",
main = "OOB error for nodesize and mtry",
key.title = title(main = "OOB error", cex.main = 1),
plot.axes = {axis(1);axis(2);points(x0,y0,pch="x",cex=1,font=2);
points(x,y,pch=16,cex=.25)})
}
## plot the surface
plot.tune(o)
}