Package

r-mlr

Description

Machine Learning in R. 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.

Note