blood

Data taken from the Blood Transfusion Service Center in Hsin-Chu City in Taiwan

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

blood function

A data frame containing 748 rows and 5 columns.
<dl>
 <dt>recency</dt>
<dd>Months since last donation</dd> <dt>frequency</dt>
<dd>Total number of donations</dd> <dt>total</dt>
<dd>Total blood donated (in c.c.)</dd> <dt>time</dt>
<dd>Months since first donation</dd> <dt>donation.crit</dt>
<dd>Criterion: Did the person donate blood (in March 2007)?
Values: <code>0</code>/no vs. <code>1</code>/yes (76.2% vs.\ 23.8%).</dd>
</dl>

Format

Blood donation data — blood

A data frame containing 748 rows and 5 columns.
<dl>
 <dt>recency</dt>
<dd>Months since last donation</dd>

 <dt>frequency</dt>
<dd>Total number of donations</dd>

 <dt>total</dt>
<dd>Total blood donated (in c.c.)</dd>

 <dt>time</dt>
<dd>Months since first donation</dd>

 <dt>donation.crit</dt>
<dd>Criterion: Did the person donate blood (in March 2007)?
Values: <code>0</code>/no vs. <code>1</code>/yes (76.2% vs.\ 23.8%).</dd>


</dl>

blood: Blood donation data

Description

Usage

Arguments

Format

See Also