variable.names.outliertree

Returns the variable names from the data to which an OutlierTree model was fitted.
Columns will be returned in the following order according to their types, regardless of the order
that they had in theoriginal input data:<ul>
<li>1. Numeric.</li>
<li>2. Date.</li>
<li>3. Timestamp (POSIXct, POSIXlt).</li>
<li>4. Categorical.</li>
<li>5. Boolean / logical.</li>
<li>6. Ordinal.</li>
</ul>

Outlier detection method that flags suspicious values within observations,
constrasting them against the normal values in a user-readable format, potentially
describing conditions within the data that make a given outlier more rare.
Full procedure is described in Cortes (2020) <doi:10.48550/arXiv.2001.00636>.
Loosely based on the 'GritBot' <https://www.rulequest.com/gritbot-info.html> software.

David Cortes

outliertree

Explainable Outlier Detection Through Decision Tree Conditioning

variable.names.outliertree function

<dl><dt>object</dt>
<dd>An OutlierTree model as returned by function outlier.tree.</dd>
<dt>...</dt>
<dd>Not used.</dd></dl>

Arguments

Get Variable Names for OutlierTree Model — variable.names.outliertree

<dl>

<dt>object</dt>
<dd>An OutlierTree model as returned by function outlier.tree.</dd>


<dt>...</dt>
<dd>Not used.</dd>

</dl>

variable.names.outliertree: Get Variable Names for OutlierTree Model

Description

Usage

Value

Arguments