select_best_tree

<code>select_best_tree</code> selects (looks up and identifies) the best tree
from the set (or &#8220;fan&#8221;) of FFTs contained in the current <code>FFTrees</code> object <code>x</code>,
an existing type of <code>data</code> ('train' or 'test'), and
a <code>goal</code> for which corresponding statistics are available
in the designated <code>data</code> type (in <code>x$trees$stats</code>).

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

select_best_tree function

<dl><dt>x</dt>
<dd>An <code>FFTrees</code> object.</dd>
<dt>data</dt>
<dd>character. Must be either "train" or "test".</dd>
<dt>goal</dt>
<dd>character. A goal to maximize or minimize when selecting a tree from an existing <code>x</code>
(for which values exist in <code>x$trees$stats</code>).</dd></dl>

Arguments

Select the best tree (from the current set) — select_best_tree

<dl>

<dt>x</dt>
<dd>An <code>FFTrees</code> object.</dd>


<dt>data</dt>
<dd>character. Must be either "train" or "test".</dd>


<dt>goal</dt>
<dd>character. A goal to maximize or minimize when selecting a tree from an existing <code>x</code>
(for which values exist in <code>x$trees$stats</code>).</dd>

</dl>

select_best_tree: Select the best tree (from the current set)

Description

Usage

Value

Arguments

Details

See Also