fact_clean

Clean factor variables in prediction data

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

fact_clean function

<dl><dt>data.train</dt>
<dd>A training dataset</dd>
<dt>data.test</dt>
<dd>A testing dataset</dd>
<dt>show.warning</dt>
<dd>logical</dd></dl>

Arguments

Clean factor variables in prediction data — fact_clean

<dl>

<dt>data.train</dt>
<dd>A training dataset</dd>


<dt>data.test</dt>
<dd>A testing dataset</dd>


<dt>show.warning</dt>
<dd>logical</dd>

</dl>

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fact_clean: Clean factor variables in prediction data

Description

Usage

Arguments