Learn how to use the Naive Bayes Classifier for fast and accurate classification in your machine learning projects. Start Reading Now!
Naive Bayes classifiers, a family of classifiers that are based on the popular Bayes’ probability theorem, are known for creating simple yet well performing models, especially in the fields of document classification and disease prediction. In this first part of a series, we will take a look a...
Figure 4.35.Data mining process for naïve Bayes algorithm. Step 4: Execution and Interpretation The process shown inFig. 4.35has three result outputs: a model description, performance vector, and labeled dataset. The labeled dataset contains the test dataset with the predicted class as an added...
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The objective function in the naive Bayes probability is to maximize the posterior probability given the training data in order to formulate the decision rule. To continue with our example above, we can formulate the decision rule based on the posterior probabilities as follows: ...