trainLearner

[<code><a rd-options="" href="/link/RLearner?package=mlr&version=2.9" data-mini-rdoc="mlr::RLearner">RLearner</a></code>]
Wrapped learner.

.learner

[<code><a rd-options="" href="/link/Task?package=mlr&version=2.9" data-mini-rdoc="mlr::Task">Task</a></code>]
Task to train learner on.

.task

[<code>integer</code>]
Subset of cases for training set, index the task with this.
You probably want to use <code><a rd-options="" href="/link/getTaskData?package=mlr&version=2.9" data-mini-rdoc="mlr::getTaskData">getTaskData</a></code> for this purpose.

.subset

[<code>numeric</code>]
Weights for each observation.

.weights

[any]
Additional (hyper)parameters, which need to be passed to the underlying train function.


Mainly for internal use. Trains a wrapped learner on a given training set.
You have to implement this method if you want to add another learner to this package.


Interface to a large number of classification and regression
techniques, including machine-readable parameter descriptions. There is
also an experimental extension for survival analysis, clustering and
general, example-specific cost-sensitive learning. Generic resampling,
including cross-validation, bootstrapping and subsampling. Hyperparameter
tuning with modern optimization techniques, for single- and multi-objective
problems. Filter and wrapper methods for feature selection. Extension of
basic learners with additional operations common in machine learning, also
allowing for easy nested resampling. Most operations can be parallelized.

Bernd Bischl

Machine Learning in R

trainLearner function

[<code><a rd-options='' href='RLearner'>RLearner</a></code>]
Wrapped learner.

[<code><a rd-options='' href='Task'>Task</a></code>]
Task to train learner on.

[<code>integer</code>]
Subset of cases for training set, index the task with this.
You probably want to use <code><a rd-options='' href='getTaskData'>getTaskData</a></code> for this purpose.

trainLearner: Train an R learner.

Description

Usage

Arguments

Value

Details