bigrf (version 0.1-12)

fastimp-methods: Compute Fast (Gini) Variable Importance

Description

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.

Usage

"fastimp"(forest)

Arguments

forest

A random forest of class "bigcforest".

Value

A numeric vector containing the fast (Gini) variable importance measures for each variable.

Methods

signature(forest = "bigcforest")

Compute the fast (Gini) variable importance for a classification random forest.

References

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.

See Also

varimp

Examples

# 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)