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Building Probabilistic Graphical Models with Python by Kiran R Karkera

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The Naive Bayes model

We can sum this up by saying that a graph can be seen from the following two viewpoints:

  • Factorization: This is where a graph allows a distribution to be represented
  • I-map: This is where the independencies encoded by the graph hold in the distribution

The Naive Bayes model is the one that makes simplistic independence assumptions. We use the Naive Bayes model to perform binary classification Here, we are given a set of instances, where each instance consists of a set of features The Naive Bayes model and a class The Naive Bayes model. The task in classification is to predict ...

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