UseProbability

<p>Computes the performance across resamples when class
probabilities can be computed.</p>

misc

Provides a novel framework to able to automatically develop and deploy
an accurate Multiple Classifier System based on the feature-clustering
distribution achieved from an input dataset. 'D2MCS' was developed focused on
four main aspects: (i) the ability to determine an effective method to
evaluate the independence of features, (ii) the identification of the
optimal number of feature clusters, (iii) the training and tuning of ML
models and (iv) the execution of voting schemes to combine the outputs of
each classifier comprising the Multiple  Classifier System.

Miguel Ferreiro-D<c3><ad>az

D2MCS

Data Driving Multiple Classifier System

David Ruano-Ordás

Miguel Ferreiro-Díaz

José Ramón Méndez

University of Vigo 

UseProbability function

<p><code>D2MCS::SummaryFunction</code> -&gt; <code>UseProbability</code></p>

Super class


<div class="section" id="public-methods">
<h3>Public methods</h3><p></p><p><ul>
<li><p><a href="/link/UseProbability%24new()?package=D2MCS&version=1.0.1" data-mini-rdoc="D2MCS::UseProbability$new()"><code>UseProbability$new()</code></a></p></li>
<li><p><a href="/link/UseProbability%24execute()?package=D2MCS&version=1.0.1" data-mini-rdoc="D2MCS::UseProbability$execute()"><code>UseProbability$execute()</code></a></p></li>
<li><p><a href="/link/UseProbability%24clone()?package=D2MCS&version=1.0.1" data-mini-rdoc="D2MCS::UseProbability$clone()"><code>UseProbability$clone()</code></a></p></li>
</ul></p><p></p></div><p><details open><summary>Inherited methods</summary>
<ul>
<li><span class="pkg-link" data-pkg="D2MCS" data-topic="SummaryFunction" data-id="getMeasures"><a href="/link/D2MCS%3A%3ASummaryFunction%24getMeasures()?package=D2MCS&version=1.0.1" data-mini-rdoc="D2MCS::D2MCS::SummaryFunction$getMeasures()"><code>D2MCS::SummaryFunction$getMeasures()</code></a></span></li>
</ul>
</details></p><p><hr>
<a id="method-UseProbability-new"></a></p><div class="section" id="method-new-">
<h3>Method <code>new()</code></h3>
<p>The function defined during runtime the usage of seven
measures: 'ROC', 'Sens', 'Kappa', 'Accuracy', 'TCR_9', 'MCC' and 'PPV'.</p><div class="section" id="usage">
<h4>Usage</h4>
<p><div class="r"></div></p><pre><code>UseProbability$new()</code></pre><p></p></div><p></p>
</div><p></p><p>
</p><p><hr>
<a id="method-UseProbability-execute"></a></p><div class="section" id="method-execute-">
<h3>Method <code>execute()</code></h3>
<p>The function computes the performance across resamples using
the previously defined measures.</p><div class="section" id="usage">
<h4>Usage</h4>
<p><div class="r"></div></p><pre><code>UseProbability$execute(data, lev = NULL, model = NULL)</code></pre><p></p></div><p></p>
</div><p></p><p><div class="section" id="arguments">
<h4>Arguments</h4>
<p><div class="arguments"></div></p><dl>
<dt><code>data</code></dt>
<dd><p>A <a href="/link/data.frame?package=D2MCS&version=1.0.1" data-mini-rdoc="D2MCS::data.frame">data.frame</a> containing the data used to compute the
performance.</p></dd></dl></div></p><p>
<dt><code>lev</code></dt>
<dd><p>An optional value used to define the levels of the target
class.</p></dd></p><p>
<dt><code>model</code></dt>
<dd><p>An optional value used to define the M.L. model used.</p></dd></p><p>
</p><p></p>

<div class="section" id="returns">
<h4>Returns</h4>
<p>A vector of performance estimates.</p>
</div><p></p><p></p><p><hr>
<a id="method-UseProbability-clone"></a></p><div class="section" id="method-clone-">
<h3>Method <code>clone()</code></h3>
<p>The objects of this class are cloneable with this method.</p><div class="section" id="usage">
<h4>Usage</h4>
<p><div class="r"></div></p><pre><code>UseProbability$clone(deep = FALSE)</code></pre><p></p></div><p></p>
</div><p></p><p><div class="section" id="arguments">
<h4>Arguments</h4>
<p><div class="arguments"></div></p><dl>
<dt><code>deep</code></dt>
<dd><p>Whether to make a deep clone.</p></dd></dl></div></p><p>
</p><p></p>
<p></p><p>
</p>

Methods

Compute performance across resamples. — UseProbability


<div class='section' id='public-methods'>
<h3>Public methods</h3>

<ul>
<li><p><a href='#method-UseProbability-new'><code>UseProbability$new()</code></a></p></li>
<li><p><a href='#method-UseProbability-execute'><code>UseProbability$execute()</code></a></p></li>
<li><p><a href='#method-UseProbability-clone'><code>UseProbability$clone()</code></a></p></li>
</ul>

</div><p><details open><summary>Inherited methods</summary>
<ul>
<li><span class="pkg-link" data-pkg="D2MCS" data-topic="SummaryFunction" data-id="getMeasures"><a href='../../D2MCS/html/SummaryFunction.html#method-SummaryFunction-getMeasures'><code>D2MCS::SummaryFunction$getMeasures()</code></a></span></li>
</ul>
</details>

<hr>
<a id="method-UseProbability-new"></a></p><div class='section' id='method-new-'>
<h3>Method <code>new()</code></h3>
<p>The function defined during runtime the usage of seven
measures: 'ROC', 'Sens', 'Kappa', 'Accuracy', 'TCR_9', 'MCC' and 'PPV'.</p><div class='section' id='usage'>
<h4>Usage</h4>
<p><div class="r"></p><pre><code>UseProbability$new()</code></pre><p></div></p>
</div>


</div><p><hr>
<a id="method-UseProbability-execute"></a></p><div class='section' id='method-execute-'>
<h3>Method <code>execute()</code></h3>
<p>The function computes the performance across resamples using
the previously defined measures.</p><div class='section' id='usage'>
<h4>Usage</h4>
<p><div class="r"></p><pre><code>UseProbability$execute(data, lev = NULL, model = NULL)</code></pre><p></div></p>
</div>

<div class='section' id='arguments'>
<h4>Arguments</h4>
<p><div class="arguments"></p><dl>
<dt><code>data</code></dt>
<dd><p>A <a href='https://rdrr.io/r/base/data.frame.html'>data.frame</a> containing the data used to compute the
performance.</p></dd>


<dt><code>lev</code></dt>
<dd><p>An optional value used to define the levels of the target
class.</p></dd>


<dt><code>model</code></dt>
<dd><p>An optional value used to define the M.L. model used.</p></dd>


</dl><p></div></p>
</div>
<div class='section' id='returns'>
<h4>Returns</h4>
<p>A vector of performance estimates.</p>
</div>

</div><p><hr>
<a id="method-UseProbability-clone"></a></p><div class='section' id='method-clone-'>
<h3>Method <code>clone()</code></h3>
<p>The objects of this class are cloneable with this method.</p><div class='section' id='usage'>
<h4>Usage</h4>
<p><div class="r"></p><pre><code>UseProbability$clone(deep = FALSE)</code></pre><p></div></p>
</div>

<div class='section' id='arguments'>
<h4>Arguments</h4>
<p><div class="arguments"></p><dl>
<dt><code>deep</code></dt>
<dd><p>Whether to make a deep clone.</p></dd>


</dl><p></div></p>
</div>

</div>


UseProbability: Compute performance across resamples.

Description

Arguments

Super class

Methods

Public methods

Method `new()`

Usage

Method `execute()`

Usage

Arguments

Returns

Method `clone()`

Usage

Arguments

See Also

Description

Arguments

Super class

Methods

Public methods

Method new()

Usage

Method execute()

Usage

Arguments

Returns

Method clone()

Usage

Arguments

See Also

Method `new()`

Method `execute()`

Method `clone()`