The ExampleSet that was given as input is passed through without changes. The model can now be applied to unlabelled data to generate predictions. The Naive Bayes classification model is delivered from this output port. The alternative Operator Naive Bayes (Kernel) is a variant of Naive Bayes where multiple Gaussians are combined, to create a kernel density. This Operator uses Gaussian probability densities to model the Attribute data. To complete the probability model, it is necessary to make some assumption about the conditional probability distributions for the individual Attributes, given the class. The independence assumption vastly simplifies the calculations needed to build the Naive Bayes probability model. Strictly speaking, this assumption is rarely true (it's "naive"!), but experience shows that the Naive Bayes classifier often works well. The fundamental assumption of Naive Bayes is that, given the value of the label (the class), the value of any Attribute is independent of the value of any other Attribute. Typical use cases involve text categorization, including spam detection, sentiment analysis, and recommender systems. It is simple to use and computationally inexpensive. Naive Bayes is a high-bias, low-variance classifier, and it can build a good model even with a small data set. This Operator generates a Naive Bayes classification model.
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