Description Usage Arguments Value Methods References Examples
Compute outlier scores for each class of examples used to train a random forest. Outliers are defined as examples whose proximities to other examples in the same class are small.
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forest |
A random forest of class |
trace |
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A numeric vector containing the outlier scores for each training example. Higher scores indicate greater dissimilarity from other training examples in the same class.
signature(forest = "bigcforest")
Compute outlier scores for a classification random forest.
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 15 16 17 18 19 20 21 22 23 24 25 | # Classify cars in the Cars93 data set by type (Compact, Large,
# Midsize, Small, Sporty, or Van).
# Load data.
data(Cars93, package="MASS")
x <- Cars93
y <- Cars93$Type
# Select variables with which to train model.
vars <- c(4:22)
# Run model, grow 30 trees.
forest <- bigrfc(x, y, ntree=30L, varselect=vars, cachepath=NULL)
# Calculate proximity matrix and scaling co-ordinates, and plot
# them.
prox <- proximities(forest, cachepath=NULL)
scale <- scaling(prox)
plot(scale, col=as.integer(y) + 2, pch=as.integer(y) + 2)
# Calculate outlier scores, and circle the top 20% percent of
# them in red.
outscores <- outliers(forest)
points(scale[outscores > quantile(outscores, probs=0.8), ],
col=2, pch=1, cex=1.5)
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