Contains several functions that allow you
to create, visualise, and test Fast and Frugal Trees (FFTrees). FFTrees are very simple decision trees for
classifying cases (i.e.; breast cancer patients) into one of two classes (e.g.;
no cancer vs. true cancer) based on a small number of cues (e.g.; test results). FFTrees can be preferable to more complex algorithms (such as
logistic regression) because they are easy to communicate and implement, and are
robust against noisy data.

Create, visualise, and test fast and frugal decision trees (FFTrees). FFTrees are very simple decision trees for
classifying cases (i.e.; breast cancer patients) into one of two classes (e.g.;
no cancer vs. true cancer) based on a small number of cues (e.g.; test results). FFTrees can be preferable to more complex algorithms because they are easy to communicate, require very little information, and are
robust against overfitting.
rpart, yarrr, MASS, circlize, mlbench, graphics, randomForest,
igraph, snow, snowfall, e1071, glmnet

rpart, yarrr, MASS, circlize, mlbench, graphics, randomForest,
igraph, snow, snowfall, e1071

Generate, Visualise, and Evaluate Fast-and-Frugal Decision Trees
c(person("Nathaniel", "Phillips", role = c("aut", "cre"),
email = "Nathaniel.D.Phillips.is@gmail.com"),
person("Hansjoerg", "Neth", role = "aut"),
person("Jan", "Woike", role = "aut"),
person("Wolfgang", "Gaissmaer", role = "aut"))
Create, visualize, and test fast-and-frugal decision trees (FFTs). FFTs are very simple decision trees for
classifying cases (i.e.; breast cancer patients) into one of two classes (e.g.;
no cancer vs. true cancer) based on a small number of cues (e.g.; test results). FFTs can be preferable to more complex algorithms because they are easy to communicate, require very little information, and are
robust against overfitting.
rpart, yarrr, MASS, circlize, mlbench, parallel, graphics,
randomForest, igraph, e1071, glmnet,stringr,progress
Nathaniel Phillips [aut, cre],
Hansjoerg Neth [aut],
Jan Woike [aut],
Wolfgang Gaissmaer [aut]