FFTrees (version 1.3.5)

FFForest: Creates a forest of fast and frugal decision trees

Description

This function is currently in development. The idea is to generate a random forest of fast and frugal trees from many splits of the training dataset.

Usage

FFForest(formula = NULL, data = NULL, data.test = NULL, max.levels = 5,
  ntree = 10, train.p = 0.5, algorithm = "ifan", goal = "wacc",
  goal.chase = "wacc", sens.w = 0.5, verbose = TRUE, cpus = 1,
  comp = FALSE, do.lr = TRUE, do.cart = TRUE, do.rf = TRUE,
  do.svm = TRUE, rank.method = NULL, hr.weight = NULL)

Arguments

formula

formula. A formula specifying a binary criterion as a function of multiple variables

data

dataframe. A dataframe containing variables in formula

data.test

dataframe. An optional dataframe of test data

max.levels

integer. Maximum number of levels considered for the trees.

ntree

integer. Number of trees to create.

train.p

numeric. What percentage of the data should be used to fit each tree? Smaller values will result in more diverse trees.

algorithm

string. The algorith uses to create FFTs. See arguments in FFTrees()

goal

character. A string indicating the statistic to maximize when selecting final trees: "acc" = overall accuracy, "bacc" = balanced accuracy, "d" = d-prime

goal.chase

character. A string indicating the statistic to maximize when constructing trees: "acc" = overall accuracy, "wacc" = weighted accuracy, "bacc" = balanced accuracy

sens.w

numeric. How much weight to give to maximizing hits versus minimizing false alarms (between 0 and 1)

verbose

logical. Should progress reports be printed?

cpus

integer. Number of cpus to use. Any value larger than 1 will initiate parallel calculations in snowfall.

comp, do.lr, do.cart, do.rf, do.svm

logical. See arguments in FFTrees()

rank.method, hr.weight

depricated arguments

Value

An object of class FFForest with the following elements...

Examples

# NOT RUN {
# }
# NOT RUN {
cancer.fff <- FFForest(formula = diagnosis ~.,
                     data = breastcancer,
                     ntree = 10,
                     train.p = .5,
                     cpus = 1)
# }
# NOT RUN {

# }

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