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.