This package provides an interface to a large number of classification and regression techniques. These techniques include machine-readable parameter descriptions. There is also an experimental extension for survival analysis, clustering and general, example-specific cost-sensitive learning. Also included:

• 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.