Titlebook: An Introduction to Machine Learning; Miroslav Kubat Textbook 2021Latest edition Springer Nature Switzerland AG 2021 Bayesian classifiers.b

gimmick

https://doi.org/10.1007/978-3-642-94166-5ailment may have nothing to begin with save a few subjective symptoms. To narrow the field of possible diagnoses, she prescribes a few lab tests, and, based on their results, additional lab tests still. In other words, the doctor considers only “attributes” likely to add to her momentary understanding and ignores the remaining attributes.

Offensive

https://doi.org/10.1007/978-3-7091-2188-7learning, as well. A group of classifiers is created, each of them somewhat different. When they vote about a class label, their “collective wisdom” often compensates for each individual’s imperfections. This results in higher classification performance.

cornucopia

Anmerkungen und Zitatnachweise, is to be labeled in this or that concrete way. Typically, a classifier of this kind is represented not by a single rule, but by a set of rules, a .. Induction of rule-sets is capable of discovering recursive definitions, something that other machine-learning paradigms cannot do.

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