Supervised Learning

Context

With Supervised Learning, the objective is to find the best probabilistic characterization of a Target Node, i.e., to produce a useful predictive model. This differs from Unsupervised Structural Learning, which attempts to find the best representation of the Joint Probability Distribution sampled by observations (or particles) recorded in a dataset. In early editions of BayesiaLab, Supervised Learning was also known as Characterization of the Target Node.

Supervised Learning Algorithms Available in BayesiaLab

• Augmented Markov Blanket

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• Augmented Naive Bayes

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• Markov Blanket

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• Minimal Augmented Markov Blanket

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• Naive Bayes

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• Semi-Supervised Learning

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• Sons and Spouses

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• Tree Augmented Naive Bayes

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