Abstract
Like decision trees, k-nearest neighbors (KNN) is a non-parametric algorithm that can perform classification and regression.
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References
A. Burkov, The Hundred-Page Machine Learning Book (Andriy Burkov, 2019)
T.-A. Timbers, T. Campbell, M. Lee, Data Science: A first introduction, (2021). [Online]. Available: https://ubc-dsci.github.io/introduction-to-datascience/regression1.html#exploring-a-data-set-1
J. Brownlee, Master Machine Learning Algorithms: Discover how they Work and Implement Them from Scratch (Jason Brownlee, 2016)
J. Grus, Data Science from Scratch: First Principles with Python (O’Reilly Media, 2015)
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El Morr, C., Jammal, M., Ali-Hassan, H., El-Hallak, W. (2022). K-Nearest Neighbors. In: Machine Learning for Practical Decision Making. International Series in Operations Research & Management Science, vol 334. Springer, Cham. https://doi.org/10.1007/978-3-031-16990-8_10
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DOI: https://doi.org/10.1007/978-3-031-16990-8_10
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