How to use parRF method so random forest will run faster

Question

I would like to run random forest on a large data set: 100k * 400. When I use random forest it takes a lot of time. Can I use parRF method from caret package in order to reduce running time? What is the right syntax for that? Here is an example dataframe: ``` dat <- read.table(text = " TargetVar Var1 Var2 Var3 0 0 0 7 0 0 1 1 0 1 0 3 0 1 1 7 1 0 0 5 1 0 1 1 1 1 0 0 1 1 1 6 0 0 0 8 0 0 1 5 1 1 1 4 0 0 1 2 1 0 0 9 1 1 1 2 ", header = TRUE) ``` I tried: ``` library('caret') m<-randomForest(TargetVar ~ Var1 + Var2 + Var3, data = dat, ntree=100, importance=TRUE, method='parRF') ``` But I don't see too much of a difference. Any Ideas?

Accepted Answer

The reason that you don't see a difference is that you aren't using the `caret` package. You do load it into your environment with the `library()` command, but then you run `randomForest()` which doesn't use caret.

I'll suggest starting by creating a data frame (or data.table) that contains only your input variables, and a vector containing your outcomes. I'm referring to the recently updated caret docs.

```
x <- data.frame(dat$Var1, dat$Var2, dat$Var3)
y <- dat$TargetVar
```

Next, verify that you have the `parRF` method available. I didn't until I updated my caret package to the most recent version (6.0-29).

```
library("randomForest")
library("caret")
names(getModelInfo())
```

You should see `parRF` in the output. Now you're ready to create your training model.

```
library(foreach)

rfParam <- expand.grid(ntree=100, importance=TRUE)

m <- train(x, y, method="parRF", tuneGrid=rfParam)
```

How to use parRF method so random forest will run faster

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