creditapproval

datasets

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.

Hansjoerg Neth

FFTrees

Generate, Visualise, and Evaluate Fast-and-Frugal Decision Trees

Nathaniel Phillips

Jan Woike

Wolfgang Gaissmaier

creditapproval function

<p>A data frame containing 690 rows and 15 columns</p>

Format

Credit approval data — creditapproval

Credit approval data

creditapproval: Credit approval data

Description

Usage

Arguments

Format

See Also