heart.train

<p>Training data for a binary prediction model (here: FFT) on (a subset of) the <code>heartdisease</code> data.
The complementary subset for model testing is <code>heart.test</code>.
The data in <code>heartdisease</code> contains both subsets.</p>

datasets

Create, visualize, and test fast-and-frugal decision trees (FFTs) using the algorithms and methods described by Phillips, Neth, Woike & Gaissmaier (2017), <doi:10.1017/S1930297500006239>.
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 understand and communicate, and are robust against overfitting.

Hansjoerg Neth

FFTrees

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

Nathaniel Phillips

Jan Woike

Wolfgang Gaissmaier

heart.train function

<p>A data frame containing 150 rows and 14 columns (see <code>heartdisease</code> for details).</p>

Format

Heart disease training data — heart.train

Heart disease training data

heart.train: Heart disease training data

Description

Usage

Arguments

Format

See Also