Abstract
The introduction to Chap. 7 presented the types of machine learning methods and described lazy classification for numerical data. What about nominal features or textual data? In this Chapter, we will begin to explore some classification techniques for categorical data. Specifically, we will (1) present the Naive Bayes algorithm; (2) review its assumptions; (3) discuss Laplace estimation; and (4) illustrate the Naive Bayesian classifier on a Head and Neck Cancer Medication case-study.
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References
Kidwell , David A. (2013) Lazy Learning, Springer Science & Business Media, ISBN 9401720533, 9789401720533
Aggarwal, Charu C. (ed.) (2015) Data Classification: Algorithms and Applications, Chapman & Hall/CRC, ISBN 1498760589, 9781498760584
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© 2018 Ivo D. Dinov
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Dinov, I.D. (2018). Probabilistic Learning: Classification Using Naive Bayes. In: Data Science and Predictive Analytics. Springer, Cham. https://doi.org/10.1007/978-3-319-72347-1_8
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DOI: https://doi.org/10.1007/978-3-319-72347-1_8
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