  This paper introduces scikit-hubness, a Python package for efficient nearest
neighbor search in high-dimensional spaces. Hubness is an aspect of the curse
of dimensionality, and is known to impair various learning tasks, including
classification, clustering, and visualization. scikit-hubness provides
algorithms for hubness analysis ("Is my data affected by hubness?"), hubness
reduction ("How can we improve neighbor retrieval in high dimensions?"), and
approximate neighbor search ("Does it work for large data sets?"). It is
integrated into the scikit-learn environment, enabling rapid adoption by
Python-based machine learning researchers and practitioners. Users will find
all functionality of the scikit-learn neighbors package, plus additional
support for transparent hubness reduction and approximate nearest neighbor
search. scikit-hubness is developed using several quality assessment tools and
principles, such as PEP8 compliance, unit tests with high code coverage,
continuous integration on all major platforms (Linux, MacOS, Windows), and
additional checks by LGTM. The source code is available at
https://github.com/VarIr/scikit-hubness under the BSD 3-clause license. Install
from the Python package index with $ pip install scikit-hubness.
