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.
rpart, yarrr, circlize, parallel, graphics, randomForest,
igraph, e1071, stringr, progress

rpart, yarrr, circlize, parallel, graphics, randomForest,
igraph, e1071, stringr, progress, pROC, caret

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.
rpart, circlize, parallel, graphics, randomForest, igraph,
e1071, crayon, scales, tibble, stringr, progress, pROC, caret,
dplyr, testthat, magrittr, tidyselect

Create, visualize, and test fast-and-frugal decision trees (FFTs) using methods described in Phillips, Neth, Woike & Gaissmaier. (2017). 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.
rpart, graphics, randomForest, e1071, crayon, scales, tibble,
stringr, progress, caret, dplyr, testthat, magrittr, tidyselect

c(person("Nathaniel", "Phillips", role = c("aut"), email = "Nathaniel.D.Phillips.is@gmail.com", comment = c(ORCID = "0000-0002-8969-7013")),
person("Hansjoerg", "Neth", role = c("aut", "cre"), email = "h.neth@uni.kn", comment = c(ORCID = "0000-0001-5427-3141")),
person("Jan", "Woike", role = "aut", comment = c(ORCID = "0000-0002-6816-121X")),
person("Wolfgang", "Gaissmaier", role = "aut", comment = c(ORCID = "0000-0001-6273-178X")))
Create, visualize, and test fast-and-frugal decision trees (FFTs) using the algorithms and methods described in Phillips, Neth, Woike & Gaissmaier (2017).
FFTs are simple and transparent decision trees for solving binary classification problems.
FFTs can be preferable to more complex algorithms because they require very little information, are easy to communicate, and are robust against overfitting.
caret, rpart, randomForest, e1071, crayon, graphics, progress,
scales, dplyr, knitr, magrittr, stringr, testthat, tibble,
tidyselect
Nathaniel Phillips [aut] (<https://orcid.org/0000-0002-8969-7013>),
Hansjoerg Neth [aut, cre] (<https://orcid.org/0000-0001-5427-3141>),
Jan Woike [aut] (<https://orcid.org/0000-0002-6816-121X>),
Wolfgang Gaissmaier [aut] (<https://orcid.org/0000-0001-6273-178X>)