interactions-methods: Compute Variable Interactions

In gboris/bigrf: Big Random Forests: Classification and Regression Forests for Large Data Sets

Description

Compute the interactions between variables, using an experimental method described by Breiman and Cutler. Variables m and n interact if a split on variable m in a tree makes a split on n either systematically less possible or more possible.

Usage

## S4 method for signature 'bigcforest'
interactions(forest)

Arguments

forest

A random forest of class "bigcforest".

Value

A symmetrical matrix of interactions between variables. A large positive number indicates that a split on one variable inhibits a split on the other variable, and conversely. This could indicate that the two variables are strongly correlated.

Methods

signature(forest = "bigcforest")

Compute variable interactions 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.

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 interactions.
inter <- interactions(forest)

gboris/bigrf documentation built on May 16, 2019, 10:14 p.m.