Description Usage Arguments Value References Examples
View source: R/generateSyntheticClass.R
To use Random Forests for unsupervised learning, the training set x
is treated as a single class. This function creates a synthetic second class for classification by sampling at random from the univariate distributions of the original data. This is useful, for example, for clustering.
1 |
x |
A |
... |
If |
A list containing the following components:
x |
The two-class training set, comprising the original training set and the synthesized second class. It will be an object of the same type as the argument |
y |
A factor vector that labels the two classes in |
Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
Breiman, L. & Cutler, A. (n.d.). Random Forests. Retrieved from http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | # Perform unsupervised learning on the Cars93 data set.
# Load data.
data(Cars93, package="MASS")
# Create second synthetic class for unsupervised learning.
newdata <- generateSyntheticClass(Cars93)
# Select variables with which to train model.
vars <- c(4:22)
# Run model, grow 30 trees.
forest <- bigrfc(newdata$x, newdata$y, ntree=30L, varselect=vars,
cachepath=NULL)
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