pudu: A Python library for agnostic feature selection and explainability of Machine Learning spectroscopic problems

Python Submitted 10 July 2023 • Published 12 December 2023
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Authors

Enric Grau-Luque (0000-0002-8357-5824), Ignacio Becerril-Romero (0000-0002-7087-6097), Alejandro Perez-Rodriguez (0000-0002-3634-1355), Maxim Guc (0000-0002-2072-9566), Victor Izquierdo-Roca (0000-0002-5502-3133)

Citation

Grau-Luque et al., (2023). pudu: A Python library for agnostic feature selection and explainability of Machine Learning spectroscopic problems. Journal of Open Source Software, 8(92), 5873, https://doi.org/10.21105/joss.05873

@article{Grau-Luque2023, doi = {10.21105/joss.05873}, url = {https://doi.org/10.21105/joss.05873}, year = {2023}, publisher = {The Open Journal}, volume = {8}, number = {92}, pages = {5873}, author = {Enric Grau-Luque and Ignacio Becerril-Romero and Alejandro Perez-Rodriguez and Maxim Guc and Victor Izquierdo-Roca}, title = {pudu: A Python library for agnostic feature selection and explainability of Machine Learning spectroscopic problems}, journal = {Journal of Open Source Software} }
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Spectroscopy Machine Learning Explainability and intepretability Classification and regression

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Journal of Open Source Software is part of Open Journals, which is a NumFOCUS-sponsored project.

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ISSN 2475-9066