learners

All supported learners can be found by <code><a rd-options="" href="/link/listLearners?package=mlr&version=2.10" data-mini-rdoc="mlr::listLearners">listLearners</a></code> or as a table
in the tutorial appendix: <a href="http://mlr-org.github.io/mlr-tutorial/release/html/integrated_learners/">http://mlr-org.github.io/mlr-tutorial/release/html/integrated_learners/</a>.

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

learners function

All supported learners can be found by <code><a rd-options='' href='listLearners'>listLearners</a></code> or as a table
in the tutorial appendix: <a href = 'http://mlr-org.github.io/mlr-tutorial/release/html/integrated_learners/'>http://mlr-org.github.io/mlr-tutorial/release/html/integrated_learners/</a>.

learners: List of supported learning algorithms.

Description

Arguments