A simulation framework for supervised learning data. The functionalities are specifically designed to let the user a maximum degrees of freedom, to ultimately fulfill the research purpose. Furthermore, feature importances of the simulation can be created on a local and a global level. This is particular interesting, for instance, to benchmark feature selection algorithms.
A Python package for chemometrics and spectroscopy that extends classical PLS regression by incorporating measurement uncertainty through neutrosophic set theory. No programming experience required – use our interactive wizard!
