apply.tree

dataframe. A model training dataset. An m x n dataframe containing n cue values for each of the m exemplars.

data

formula

dataframe. Definitions of one or more trees. The dataframe must contain the columns: cues, classes, thresholds, directions, exits.

tree.definitions

numeric. A number from 0 to 1 indicating how to weight sensitivity relative to specificity. Only used for calculating wacc values.

sens.w

numeric. A vector of length 4 specifying the costs of a hit, false alarm, miss, and correct rejection rspectively. E.g.; <code>cost.outcomes = c(0, 10, 20, 0)</code> means that a false alarm and miss cost 10 and 20 respectively while correct decisions have no cost.

cost.outcomes

dataframe. A dataframe with two columns specifying the cost of each cue. The first column should be a vector of cue names, and the second column should be a numeric vector of costs. Cues in the dataset not present in <code>cost.cues</code> are assume to have 0 cost.

cost.cues

logical. What should be predicted if all cue values in tree are NA? Default is FALSE

allNA.pred

Applies a fast-and-frugal tree to a dataset and generates several accuracy statistics

Create, visualize, and test fast-and-frugal decision trees (FFTs). FFTs are very simple decision trees for
binary classification problems. FFTs can be preferable to more complex algorithms because they are easy to communicate, require very little information, and are
robust against overfitting.

apply.tree: Applies a fast-and-frugal tree to a dataset and generates several accuracy statistics

Description

Usage

Arguments

Value