tune.rfsrc: Tune Random Forest for the optimal mtry and nodesize...

In randomForestSRC: Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC)

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-sample error. Applies to all families.

Usage

## S3 method for class 'rfsrc'
tune(formula, data,
  mtryStart = ncol(data) / 2,
  nodesizeTry = c(1:9, seq(10, 100, by = 5)), ntreeTry = 100,
  sampsize = function(x){min(x * .632, max(150, x ^ (3/4)))},
  nsplit = 1, stepFactor = 1.25, improve = 1e-3, strikeout = 3, maxIter = 25,
  trace = FALSE, doBest = FALSE, ...)

## S3 method for class 'rfsrc'
tune.nodesize(formula, data,
  nodesizeTry = c(1:9, seq(10, 150, by = 5)), ntreeTry = 100,
  sampsize = function(x){min(x * .632, max(150, x ^ (4/5)))},
  nsplit = 1, trace = 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.
sampsize	Function specifying requested size of subsampled data. Can also be passed in as a number.
nsplit	Number of random splits used for splitting.
stepFactor	At each iteration, mtry is inflated (or deflated) by this value.
improve	The (relative) improvement in out-of-sample 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 rfsrc.fast.

Details

tune returns a matrix whose first and second columns contain the nodesize and mtry values searched and whose third column is the corresponding out-of-sample error. Uses standardized error and in the case of multivariate forests it is the averaged standardized rror over the outcomes and for competing risks it is the averaged standardized 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 rfsrc.fast which utilizes subsampling. However, while this yields a fast optimization strategy, such a solution can only be considered approximate. Users may wish to tweak various options to improve accuracy. Increasing the default sampsize will definitely help. Increasing ntreeTry (which is set to 100 for speed) may also help. It is also useful to look at contour plots of the out-of-sample error as a function of mtry and nodesize (see example below) to identify regions of the parameter space where error rate is small.

tune.nodesize returns the optimal nodesize where optimization is over nodesize only.

Author(s)

Hemant Ishwaran and Udaya B. Kogalur

See Also

rfsrc.fast

Examples

## ------------------------------------------------------------
## White wine classification example
## ------------------------------------------------------------

## load the data
data(wine, package = "randomForestSRC")
wine$quality <- factor(wine$quality)

## set the sample size manually
o <- tune(quality ~ ., wine, sampsize = 100)

## here is the optimized forest 
print(o$rf)

## visualize the nodesize/mtry OOB surface
if (library("interp", 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 = "error rate 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)

}

## ------------------------------------------------------------
## tuning for class imbalanced data problem
## - see imbalanced function for details
## - use rfq and perf.type = "gmean" 
## ------------------------------------------------------------

data(breast, package = "randomForestSRC")
breast <- na.omit(breast)
o <- tune(status ~ ., data = breast, rfq = TRUE, perf.type = "gmean")
print(o)


## ------------------------------------------------------------
## tune nodesize for competing risk - wihs data 
## ------------------------------------------------------------

data(wihs, package = "randomForestSRC")
plot(tune.nodesize(Surv(time, status) ~ ., wihs, trace = TRUE)$err)

randomForestSRC documentation built on May 31, 2023, 9:44 p.m.