randomForestSRC (version 2.8.0)

tune: Tune Random Forest for the optimal mtry and nodesize parameters

Description

Finds the optimal mtry and nodesize tuning parameter for a random forest using out-of-bag (OOB) error. Applies to all families.

Usage

# S3 method for 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, ...)

Arguments

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 strikeout allows for some tolerance in this. If a negative improvement is noted a total of strikeout times, the search is stopped. Increase this value only if you want an exhaustive search.

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 rfsrcFast.

Details

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.

See Also

rfsrcFast

Examples

# NOT RUN {
## ------------------------------------------------------------
## 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)

}

# }