Data

Data.default

Data.outForest

Extracts data with optionally replaced outliers from object of class "outForest".

Provides a random forest based implementation of the method
described in Chapter 7.1.2 (Regression model based anomaly detection)
of Chandola et al. (2009) <doi:10.1145/1541880.1541882>. It works as
follows: Each numeric variable is regressed onto all other variables
by a random forest. If the scaled absolute difference between observed
value and out-of-bag prediction of the corresponding random forest is
suspiciously large, then a value is considered an outlier. The package
offers different options to replace such outliers, e.g. by realistic
values found via predictive mean matching. Once the method is trained
on a reference data, it can be applied to new data.

Michael Mayer

outForest

Multivariate Outlier Detection and Replacement

Data function

<dl><dt>object</dt>
<dd>An object of class "outForest".</dd>
<dt>...</dt>
<dd>Arguments passed from or to other methods.</dd></dl>

Arguments

<ul>
<li><code>Data(default)</code>: Default method not implemented yet.</li>
<li><code>Data(outForest)</code>: Extract data from "outForest" object.</li>
</ul>

Methods (by class)

Extracts Data — Data

<dl>

<dt>object</dt>
<dd>An object of class "outForest".</dd>


<dt>...</dt>
<dd>Arguments passed from or to other methods.</dd>

</dl>

Data: Extracts Data

Description

Usage

Value

Arguments

Methods (by class)

Examples