LearnerLm

<p>This learner is a wrapper around <code><a href="/link/stats%3A%3Alm()?package=mlexperiments&version=0.0.5" data-mini-rdoc="mlexperiments::stats::lm()">stats::lm()</a></code> in order to perform a
linear regression. There is no implementation for tuning
parameters.</p>

Provides 'R6' objects to perform parallelized hyperparameter
optimization and cross-validation. Hyperparameter optimization can be
performed with Bayesian optimization (via 'ParBayesianOptimization'
<https://cran.r-project.org/package=ParBayesianOptimization>) and grid
search. The optimized hyperparameters can be validated using k-fold
cross-validation. Alternatively, hyperparameter optimization and
validation can be performed with nested cross-validation. While
'mlexperiments' focuses on core wrappers for machine learning
experiments, additional learner algorithms can be supplemented by
inheriting from the provided learner base class.

Lorenz A. Kapsner

mlexperiments

Machine Learning Experiments

LearnerLm function

<p><code>mlexperiments::MLLearnerBase</code> -&gt; <code>LearnerLm</code></p>

Super class


<div class="section" id="public-methods">
<h3>Public methods</h3><p></p><p><ul>
<li><p><a href="/link/LearnerLm%24new()?package=mlexperiments&version=0.0.5" data-mini-rdoc="mlexperiments::LearnerLm$new()"><code>LearnerLm$new()</code></a></p></li>
<li><p><a href="/link/LearnerLm%24clone()?package=mlexperiments&version=0.0.5" data-mini-rdoc="mlexperiments::LearnerLm$clone()"><code>LearnerLm$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="mlexperiments" data-topic="MLLearnerBase" data-id="bayesian_scoring_function"><a href="/link/mlexperiments%3A%3AMLLearnerBase%24bayesian_scoring_function()?package=mlexperiments&version=0.0.5" data-mini-rdoc="mlexperiments::mlexperiments::MLLearnerBase$bayesian_scoring_function()"><code>mlexperiments::MLLearnerBase$bayesian_scoring_function()</code></a></span></li>
<li><span class="pkg-link" data-pkg="mlexperiments" data-topic="MLLearnerBase" data-id="cross_validation"><a href="/link/mlexperiments%3A%3AMLLearnerBase%24cross_validation()?package=mlexperiments&version=0.0.5" data-mini-rdoc="mlexperiments::mlexperiments::MLLearnerBase$cross_validation()"><code>mlexperiments::MLLearnerBase$cross_validation()</code></a></span></li>
<li><span class="pkg-link" data-pkg="mlexperiments" data-topic="MLLearnerBase" data-id="fit"><a href="/link/mlexperiments%3A%3AMLLearnerBase%24fit()?package=mlexperiments&version=0.0.5" data-mini-rdoc="mlexperiments::mlexperiments::MLLearnerBase$fit()"><code>mlexperiments::MLLearnerBase$fit()</code></a></span></li>
<li><span class="pkg-link" data-pkg="mlexperiments" data-topic="MLLearnerBase" data-id="predict"><a href="/link/mlexperiments%3A%3AMLLearnerBase%24predict()?package=mlexperiments&version=0.0.5" data-mini-rdoc="mlexperiments::mlexperiments::MLLearnerBase$predict()"><code>mlexperiments::MLLearnerBase$predict()</code></a></span></li>
</ul>
</details></p><p><hr>
<a id="method-LearnerLm-new"></a></p><div class="section" id="method-new-">
<h3>Method <code>new()</code></h3>
<p>Create a new <code>LearnerLm</code> object.</p><div class="section" id="usage">
<h4>Usage</h4>
<p><div class="r"></div></p><pre><code>LearnerLm$new()</code></pre><p></p></div><p></p>
</div><p></p><p><div class="section" id="details">
<h4>Details</h4>
<p>This learner is a wrapper around <code><a href="/link/stats%3A%3Alm()?package=mlexperiments&version=0.0.5" data-mini-rdoc="mlexperiments::stats::lm()">stats::lm()</a></code> in order to perform a
linear regression. There is no implementation for tuning
parameters, thus the only experiment to use <code>LearnerLm</code> for is
MLCrossValidation</p>
</div></p><p><div class="section" id="returns">
<h4>Returns</h4>
<p>A new <code>LearnerLm</code> R6 object.</p>
</div>
<div class="section" id="examples">
<h4>Examples</h4>
<p><div class="r example copy"></div></p><pre><code>LearnerLm$new()</code></pre></div></p><p></p><p></p>
<p></p><p>
</p><p><hr>
<a id="method-LearnerLm-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>LearnerLm$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

<p>This learner is a wrapper around <code><a href='https://rdrr.io/r/stats/lm.html'>stats::lm()</a></code> in order to perform a
linear regression. There is no implementation for tuning
parameters.</p>

LearnerLm R6 class — LearnerLm


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

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

</div><p><details open><summary>Inherited methods</summary>
<ul>
<li><span class="pkg-link" data-pkg="mlexperiments" data-topic="MLLearnerBase" data-id="bayesian_scoring_function"><a href='../../mlexperiments/html/MLLearnerBase.html#method-MLLearnerBase-bayesian_scoring_function'><code>mlexperiments::MLLearnerBase$bayesian_scoring_function()</code></a></span></li>
<li><span class="pkg-link" data-pkg="mlexperiments" data-topic="MLLearnerBase" data-id="cross_validation"><a href='../../mlexperiments/html/MLLearnerBase.html#method-MLLearnerBase-cross_validation'><code>mlexperiments::MLLearnerBase$cross_validation()</code></a></span></li>
<li><span class="pkg-link" data-pkg="mlexperiments" data-topic="MLLearnerBase" data-id="fit"><a href='../../mlexperiments/html/MLLearnerBase.html#method-MLLearnerBase-fit'><code>mlexperiments::MLLearnerBase$fit()</code></a></span></li>
<li><span class="pkg-link" data-pkg="mlexperiments" data-topic="MLLearnerBase" data-id="predict"><a href='../../mlexperiments/html/MLLearnerBase.html#method-MLLearnerBase-predict'><code>mlexperiments::MLLearnerBase$predict()</code></a></span></li>
</ul>
</details>

<hr>
<a id="method-LearnerLm-new"></a></p><div class='section' id='method-new-'>
<h3>Method <code>new()</code></h3>
<p>Create a new <code>LearnerLm</code> object.</p><div class='section' id='usage'>
<h4>Usage</h4>
<p><div class="r"></p><pre><code>LearnerLm$new()</code></pre><p></div></p>
</div>

<div class='section' id='details'>
<h4>Details</h4>
<p>This learner is a wrapper around <code><a href='https://rdrr.io/r/stats/lm.html'>stats::lm()</a></code> in order to perform a
linear regression. There is no implementation for tuning
parameters, thus the only experiment to use <code>LearnerLm</code> for is
MLCrossValidation</p>
</div>

<div class='section' id='returns'>
<h4>Returns</h4>
<p>A new <code>LearnerLm</code> R6 object.</p>
</div>
<div class='section' id='examples'>
<h4>Examples</h4>
<p><div class="r example copy"></p><pre><code>LearnerLm$new()

</code></pre><p></div></p>
</div>


</div><p><hr>
<a id="method-LearnerLm-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>LearnerLm$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>


LearnerLm: LearnerLm R6 class

Description

Arguments

Super class

Methods

Public methods

Method `new()`

Usage

Details

Returns

Examples

Method `clone()`

Usage

Arguments

Details

See Also

Examples

Description

Arguments

Super class

Methods

Public methods

Method new()

Usage

Details

Returns

Examples

Method clone()

Usage

Arguments

Details

See Also

Examples

Method `new()`

Method `clone()`