mlr_measures_regr.pbias

Regression measure defined as $$
 \frac{1}{n} \sum_{i=1}^n \frac{\left( t_i - r_i \right)}{\left| t_i \right|}.
$$
Good predictions score close to 0.

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

mlr_measures_regr.pbias function

This <a rd-options="" href="/link/Measure?package=mlr3&version=0.3.0" data-mini-rdoc="mlr3::Measure">Measure</a> can be instantiated via the <a rd-options="mlr3misc:Dictionary" href="/link/dictionary?package=mlr3&version=0.3.0&to=mlr3misc%3ADictionary" data-mini-rdoc="mlr3misc:Dictionary::dictionary">dictionary</a> <a rd-options="" href="/link/mlr_measures?package=mlr3&version=0.3.0" data-mini-rdoc="mlr3::mlr_measures">mlr_measures</a> or with the associated sugar function <code><a rd-options="=msr" href="/link/msr()?package=mlr3&version=0.3.0&to=%3Dmsr" data-mini-rdoc="=msr::msr()">msr()</a></code>:<pre>mlr_measures$get("pbias")
msr("pbias")
</pre>

Dictionary

<ul>
<li>Type: <code>"regr"</code></li>
<li>Range: $(-\infty, \infty)$</li>
<li>Minimize: <code>NA</code></li>
<li>Required prediction: <code>response</code></li>
</ul>

Meta Information

Percent Bias — mlr_measures_regr.pbias

Percent Bias

mlr_measures_regr.pbias: Percent Bias

Description

Arguments

Dictionary

Meta Information

See Also