is.outForest

Checks if an object inherits 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

is.outForest function

<dl><dt>x</dt>
<dd>Any object.</dd></dl>

Arguments

Type Check — is.outForest

<dl>

<dt>x</dt>
<dd>Any object.</dd>

</dl>

is.outForest: Type Check

Description

Usage

Value

Arguments

Examples