  Dealing with uncertainty in applications of machine learning to real-life
data critically depends on the knowledge of intrinsic dimensionality (ID). A
number of methods have been suggested for the purpose of estimating ID, but no
standard package to easily apply them one by one or all at once has been
implemented in Python. This technical note introduces
\texttt{scikit-dimension}, an open-source Python package for intrinsic
dimension estimation. \texttt{scikit-dimension} package provides a uniform
implementation of most of the known ID estimators based on scikit-learn
application programming interface to evaluate global and local intrinsic
dimension, as well as generators of synthetic toy and benchmark datasets
widespread in the literature. The package is developed with tools assessing the
code quality, coverage, unit testing and continuous integration. We briefly
describe the package and demonstrate its use in a large-scale (more than 500
datasets) benchmarking of methods for ID estimation in real-life and synthetic
data. The source code is available from
https://github.com/j-bac/scikit-dimension , the documentation is available from
https://scikit-dimension.readthedocs.io .
