LearnerRegrRpart

mlr_learners_regr.rpart

A <a rd-options="" href="/link/LearnerRegr?package=mlr3&version=0.1.4" data-mini-rdoc="mlr3::LearnerRegr">LearnerRegr</a> for a regression tree implemented in <code><a rd-options="rpart:rpart" href="/link/rpart%3A%3Arpart()?package=mlr3&version=0.1.4&to=rpart%3Arpart" data-mini-rdoc="rpart:rpart::rpart::rpart()">rpart::rpart()</a></code> in package rpart.
Parameter <code>xval</code> is set to 0 in order to save some computation time.

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

LearnerRegrRpart function

<a rd-options="R6:R6Class" href="/link/R6%3A%3AR6Class?package=mlr3&version=0.1.4&to=R6%3AR6Class" data-mini-rdoc="R6:R6Class::R6::R6Class">R6::R6Class</a> inheriting from <a rd-options="" href="/link/LearnerRegr?package=mlr3&version=0.1.4" data-mini-rdoc="mlr3::LearnerRegr">LearnerRegr</a>.

Format

<pre>LearnerRegrRpart$new()
mlr_learners$get("regr.rpart")
lrn("regr.rpart")
</pre>

Construction

A <a rd-options='' href='LearnerRegr'>LearnerRegr</a> for a regression tree implemented in <code><a rd-options='rpart:rpart' href='rpart::rpart()'>rpart::rpart()</a></code> in package rpart.
Parameter <code>xval</code> is set to 0 in order to save some computation time.

Regression Tree Learner — LearnerRegrRpart

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

LearnerRegrRpart: Regression Tree Learner

Description

Arguments

Format

Construction

References

See Also