heart.cost

This data further characterizes the variables (cues) in the <code>heartdisease</code> dataset.

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

heart.cost function

A data frame containing 153 rows and 14 columns.
<dl>
 <dt>cue</dt>
<dd>The name of the cue</dd> <dt>cost</dt>
<dd>The cost of the cue</dd>
 ...</dl>

Format

Cue costs for the heartdisease data — heart.cost

A data frame containing 153 rows and 14 columns.
<dl>
 <dt>cue</dt>
<dd>The name of the cue</dd>

 <dt>cost</dt>
<dd>The cost of the cue</dd>


 ...

</dl>

heart.cost: Cue costs for the heartdisease data

Description

Usage

Arguments

Format

See Also