A dataset on car evaluations based on basic features, derived from a simple hierarchical decision model.

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

car function

A data frame containing 1728 cars (rows) and 7 variables (columns).
<dl>
 <dt>buying.price</dt>
<dd>Numeric</dd> <dt>maint.price</dt>
<dd>Factor</dd> <dt>doors</dt>
<dd>Factor</dd> <dt>persons</dt>
<dd>Numeric</dd> <dt>luggage</dt>
<dd>Numeric</dd> <dt>safety</dt>
<dd>Factor</dd> <dt>acceptability</dt>
<dd>Factor</dd>
 ...</dl>

Format

Car acceptability data — car

A data frame containing 1728 cars (rows) and 7 variables (columns).
<dl>
 <dt>buying.price</dt>
<dd>Numeric</dd>

 <dt>maint.price</dt>
<dd>Factor</dd>

 <dt>doors</dt>
<dd>Factor</dd>

 <dt>persons</dt>
<dd>Numeric</dd>

 <dt>luggage</dt>
<dd>Numeric</dd>

 <dt>safety</dt>
<dd>Factor</dd>

 <dt>acceptability</dt>
<dd>Factor</dd>


 ...

</dl>

car: Car acceptability data

Description

Usage

Arguments

Format

Details

References

See Also