evaluate_class

calculate_confusion

Calculation of binary similarity metric based on confusion matrix.

We present 'FACT' (Feature Attributions for ClusTering), a
framework for unsupervised interpretation methods that can be used with an
arbitrary clustering algorithm. The package is capable of re-assigning instances to
clusters (algorithm agnostic), preserves the integrity of the data and does
not introduce additional models. 'FACT' is inspired by the principles of
model-agnostic interpretation in supervised learning. Therefore, some of the
methods presented are based on 'iml', a R Package for Interpretable Machine
Learning by Christoph Molnar, Giuseppe Casalicchio, and Bernd Bischl (2018)
<doi:10.21105/joss.00786>.

Henri Funk

FACT

Feature Attributions for ClusTering

Christian Scholbeck

Giuseppe Casalicchio

evaluate_class function

<dl><dt>actual</dt>
<dd><code>numeric</code> 
initial cluster assignments</dd>
<dt>predicted</dt>
<dd><code>numeric</code> 
cluster assignments of permuted data</dd>
<dt>metric</dt>
<dd><code>character(1)</code> 
binary score metric</dd></dl>

Arguments

<ul>
<li><code>calculate_confusion()</code>: Calculate confusion matrix</li>
</ul>

Functions

Evaluate Class — evaluate_class

<dl>

<dt>actual</dt>
<dd><code>numeric</code> 
initial cluster assignments</dd>


<dt>predicted</dt>
<dd><code>numeric</code> 
cluster assignments of permuted data</dd>


<dt>metric</dt>
<dd><code>character(1)</code> 
binary score metric</dd>

</dl>

evaluate_class: Evaluate Class

Description

Usage

Value

Arguments

Functions