Wraps an already implemented learning method from R to make it accessible to mlr.
Call this method in your constructor. You have to pass an id (name), the required
package(s), a description object for all changeable parameters (you dont have to do this for the
learner to work, but it is strongly recommended), and use property tags to define features of the learner.

RLearner

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.

RLearner: Internal construction / wrapping of learner object.

Description

Usage

Arguments

Value