This chapter explores support vector machines (SVMs), widely employed supervised learning algorithms recognized for their effectiveness in binary classification tasks. SVMs aim to find an optimal hyperplane (a decision plane that separates objects with different class memberships) that maximizes the margin between data points of different classes. The hyperplane acts as a decision boundary, with one class on each side. The margin represents the ...
© The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature 2023
A. TestasDistributed Machine Learning with PySparkhttps://doi.org/10.1007/978-1-4842-9751-3_1010. Support Vector Machine Classification with Pandas, Scikit-Learn, and PySpark
Abdelaziz Testas1
(1)
Fremont, CA, USA
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