mlr_measures_regr.mse

Measure to compare true observed response with predicted response in regression tasks.

Efficient, object-oriented programming on the
building blocks of machine learning. Provides 'R6' objects for tasks,
learners, resamplings, and measures. The package is geared towards
scalability and larger datasets by supporting parallelization and
out-of-memory data-backends like databases. While 'mlr3' focuses on
the core computational operations, add-on packages provide additional
functionality.

Michel Lang

mlr3

Machine Learning in R - Next Generation

Bernd Bischl

Jakob Richter

Patrick Schratz

Giuseppe Casalicchio

Stefan Coors

Quay Au

Martin Binder

Florian Pfisterer

Raphael Sonabend

Lennart Schneider

Marc Becker

Sebastian Fischer

mlr_measures_regr.mse function

This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function <code>msr()</code>:
<div class="sourceCode"></div><pre><code>mlr_measures$get("regr.mse")
msr("regr.mse")
</code></pre>

Dictionary

Parameters

<ul>
<li>Type: <code>"regr"</code></li>
<li>Range: $[0, \infty)$</li>
<li>Minimize: <code>TRUE</code></li>
<li>Required prediction: <code>response</code></li>
</ul>

Meta Information

Mean Squared Error — mlr_measures_regr.mse

This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function <code>msr()</code>:
<div class="sourceCode"><pre><code>mlr_measures$get("regr.mse")
msr("regr.mse")
</code></pre></div>

<ul>
<li>Type: <code>"regr"</code></li>
<li>Range: $[0, \infty)$</li>
<li>Minimize: <code>TRUE</code></li>
<li>Required prediction: <code>response</code></li>
</ul>

mlr_measures_regr.mse: Mean Squared Error

Description

Arguments

Dictionary

Parameters

Meta Information

Details

See Also