![]() ![]() We briefly describe the package and demonstrate its use in a large-scale (more than 500 datasets) benchmarking of methods for ID estimation for real-life and synthetic data.ĪbstractRegression testing comprises techniques which are applied during software evolution to uncover faults effectively and efficiently. ![]() The package is developed with tools assessing the code quality, coverage, unit testing and continuous integration. The scikit-dimension package provides a uniform implementation of most of the known ID estimators based on the scikit-learn application programming interface to evaluate the global and local intrinsic dimension, as well as generators of synthetic toy and benchmark datasets widespread in the literature. This technical note introduces scikit-dimension, an open-source Python package for intrinsic dimension estimation. 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. ![]() Dealing with uncertainty in applications of machine learning to real-life data critically depends on the knowledge of intrinsic dimensionality (ID). ![]()
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