You're reading from Interpretable Machine Learning with Python - Second Edition

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Published inOct 2023

PublisherPackt

ISBN-139781803235424

Edition2nd Edition

Concepts

Machine Learning

Author (1)

Serg Masís

Ross, B.C., 2014, Mutual Information between Discrete and Continuous Data Sets. PLoS ONE, 9: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0087357
Geurts, P., Ernst, D., and Wehenkel, L., 2006, Extremely randomized trees. Machine Learning, 63(1), 3-42: https://link.springer.com/article/10.1007/s10994-006-6226-1
Abid, A., Balin, M.F., and Zou, J., 2019, Concrete Autoencoders for Differentiable Feature Selection and Reconstruction. ICML: https://arxiv.org/abs/1901.09346
Tan, F., Fu, X., Zhang, Y., and Bourgeois, A.G., 2008, A genetic algorithm-based method for feature subset selection. Soft Computing, 12, 111-120: https://link.springer.com/article/10.1007/s00500-007-0193-8
Calzolari, M., 2020, October 12, manuel-calzolari/sklearn-genetic: sklearn-genetic 0.3.0 (Version 0.3.0). Zenodo: http://doi.org/10.5281/zenodo.4081754

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Interpretable Machine Learning with Python - Second Edition

Published in: Oct 2023Publisher: PacktISBN-13: 9781803235424

Author (1)

Serg Masís

Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. Currently, he's a climate and agronomic data scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co-founded a start-up, incubated by Harvard Innovation Labs, that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events. Whether it pertains to leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the often-missing link between data and decision-making—and machine learning interpretation helps bridge this gap robustly.
Read more about Serg Masís

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