Description Usage Arguments Value Methods References See Also Examples
Calculates variable importance using a fast method, by adding up the decreases in Gini impurity for each variable over all trees. The results are often consistent with the full variable importance calculated with varimp
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1 2 | ## S4 method for signature 'bigcforest'
fastimp(forest)
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forest |
A random forest of class |
A numeric vector containing the fast (Gini) variable importance measures for each variable.
signature(forest = "bigcforest")
Compute the fast (Gini) variable importance 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 | # 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 variable importance, including those for each out-of-bag example.
fastimportance <- fastimp(forest)
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