This package includes a scikit-learn-compatible Python implementation of ReBATE,
a suite of Relief-based feature selection algorithms for Machine Learning. These
Relief-Based algorithms (RBAs) are designed for feature weighting/selection as
part of a machine learning pipeline (supervised learning). Presently this
includes the following core RBAs: ReliefF, SURF, SURF*, MultiSURF*, and
MultiSURF. Additionally, an implementation of the iterative TuRF mechanism and
VLSRelief is included.