FFTrees (version 1.1.8)

grow.FFTrees: Grows fast and frugal trees

Description

Grows fast and frugal trees

Usage

grow.FFTrees(formula, data, rank.method = "m", repeat.cues = TRUE, hr.weight = 0.5, max.levels = 4, stopping.rule = "exemplars", stopping.par = 0.1, verbose = F)

Arguments

formula

a formula

data

A dataset

rank.method

A string indicating how to rank cues during tree construction. "m" (for marginal) means that cues will only be ranked once with the entire training dataset. "c" (conditional) means that cues will be ranked after each level in the tree with the remaining unclassified training exemplars.

repeat.cues

A logical value indicating whether or not to allow repeated cues in the tree. Only relevant when `rank.method = 'c'.

hr.weight

A value between 0 and 1 indicating how much weight to give to maximizing hit rates versus minimizing false alarm rates. Used for ranking cues in the tree.

max.levels

The maximum number of levels in the tree(s)

stopping.rule

A string indicating the method to stop growing trees. "levels" means the tree grows until a certain level. "exemplars" means the tree grows until a certain number of unclassified exemplars remain. "statdelta" means the tree grows until the change in the tree.criterion statistic is less than a specified level.

stopping.par

A number indicating the parameter for the stopping rule. For stopping.rule == "levels", this is the number of levels. For stopping rule == "exemplars", this is the smallest percentage of examplars allowed in the last level.

verbose

A logical value indicating whether or not to display progress

Value

A list of length 4. tree.definitions contains definitions of the tree(s). tree.stats contains classification statistics for the tree(s). levelout shows which level in the tree(s) each exemplar is classified. Finally, decision shows the classification decision for each tree for each exemplar

Examples

 titanic.trees <- grow.FFTrees(formula = survived ~.,
                                   data = titanic)

# Tree definitions are stored in tree.definitions

titanic.trees$tree.definitions

# Tree classification statistics are in tree.stats

titanic.trees$tree.stats

# The level at which each exemplar is classified for each tree is in levelout

titanic.trees$levelout

# The decision for each exemplar for each tree is in decision

titanic.trees$decision