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Using Naive Bayes and JRip

Mark Hall shows how to learn classification models on the hypothyroid data, using Naive Bayes and the JRip rule learner.

There are many options when configuring a Distributed Weka job. The ArffHeaderSparkJob’s configuration panel has two tabs, Spark configuration, whose options relate to how the cluster is configured, including how many partitions to make from the data and the desired level of parallelism; and ArffHeaderSparkJob, which determines how Weka parses the CSV file containing the input data, including the names of attributes and the name of the header file that is created. Another Distributed Weka template is “Spark: train and save two classifiers”, which trains Naive Bayes and JRip classifiers from the same dataset.

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Advanced Data Mining with Weka

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