iris.v

<p>A famous dataset from R.A. Fisher (1936) simplified to predict only the virginica class (i.e., as a binary classification problem).</p>

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

iris.v function

<p>A data frame containing 150 rows and 4 columns.</p>

Format

Iris data — iris.v

Iris data

iris.v: Iris data

Description

Usage

Arguments

Format

References

See Also