mlr_measures_regr.sse

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.

Marc Becker

mlr3

Machine Learning in R - Next Generation

Michel Lang

Bernd Bischl

Jakob Richter

Patrick Schratz

Giuseppe Casalicchio

Stefan Coors

Quay Au

Martin Binder

Florian Pfisterer

Raphael Sonabend

Lennart Schneider

Sebastian Fischer

Lona Koers

John Zobolas

mlr_measures_regr.sse 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.sse")
msr("regr.sse")
</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

Sum of Squared Errors — mlr_measures_regr.sse

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.sse")
msr("regr.sse")
</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>

Sum of Squared Errors

mlr_measures_regr.sse: Sum of Squared Errors

Description

Arguments

Dictionary

Parameters

Meta Information

Details

See Also