LearnerClassifFeatureless

mlr_learners_classif.featureless

A simple <a rd-options="" href="/link/LearnerClassif?package=mlr3&version=0.1.2" data-mini-rdoc="mlr3::LearnerClassif">LearnerClassif</a> which only analyses the labels during train, ignoring all features.
Hyperparameter <code>method</code> determines the mode of operation during prediction:<dl class="dl-horizontal">
<dt>mode:</dt><dd>Predicts the most frequent label. If there are two or more labels tied, randomly selects one per prediction.</dd>
<dt>sample:</dt><dd>Randomly predict a label uniformly.</dd>
<dt>weighed.sample:</dt><dd>Randomly predict a label, with probability estimated from the training distribution.</dd>
</dl>

datasets

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

LearnerClassifFeatureless function

<a rd-options="R6:R6Class" href="/link/R6%3A%3AR6Class?package=mlr3&version=0.1.2&to=R6%3AR6Class" data-mini-rdoc="R6:R6Class::R6::R6Class">R6::R6Class</a> inheriting from <a rd-options="" href="/link/LearnerClassif?package=mlr3&version=0.1.2" data-mini-rdoc="mlr3::LearnerClassif">LearnerClassif</a>.

Format

<pre>LearnerClassifFeatureless$new()
mlr_learners$get("classif.featureless")
lrn("classif.featureless")
</pre>

Construction

A simple <a rd-options='' href='LearnerClassif'>LearnerClassif</a> which only analyses the labels during train, ignoring all features.
Hyperparameter <code>method</code> determines the mode of operation during prediction:<dl class='dl-horizontal'>
<dt>mode:</dt><dd>Predicts the most frequent label. If there are two or more labels tied, randomly selects one per prediction.</dd>
<dt>sample:</dt><dd>Randomly predict a label uniformly.</dd>
<dt>weighed.sample:</dt><dd>Randomly predict a label, with probability estimated from the training distribution.</dd>
</dl>

Featureless Classification Learner — LearnerClassifFeatureless

<a rd-options='R6:R6Class' href='R6::R6Class'>R6::R6Class</a> inheriting from <a rd-options='' href='LearnerClassif'>LearnerClassif</a>.

LearnerClassifFeatureless: Featureless Classification Learner

Description

Arguments

Format

Construction

See Also