{"id":204,"date":"2012-06-28T17:13:29","date_gmt":"2012-06-28T17:13:29","guid":{"rendered":"http:\/\/garysieling.com\/blog\/?p=204"},"modified":"2012-06-28T17:13:29","modified_gmt":"2012-06-28T17:13:29","slug":"a-brief-introduction-to-weka","status":"publish","type":"post","link":"https:\/\/www.garysieling.com\/blog\/a-brief-introduction-to-weka\/","title":{"rendered":"A brief introduction to Weka"},"content":{"rendered":"

Weka is a GPL data mining tool written in Java, published by the University of Waikato. It includes an extensive series of pre-implemented machine learning algorithms, including well known classification and clustering algorithms. If you’ve ever been curious how Bayes Theorem works, this is a great tool to get up and running.<\/p>\n

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Weka uses a custom data format, called ARFF files (Attribute Relation File Format). This, in essence, specifies a table of data, along with a CSV style data listing. The data types are scaled down from a type of database: numerics, strings, dates, and nominal attributes (i.e. an equivalent to enumeration or pick list).<\/p>\n

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You can connect to any database with a JDBC connection string, provided the appropriate jars are on the classpath. Weka ships with a file called\u00a0DatabaseUtils.props, which maps database types to the Weka types listed above.<\/p>\n

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Once you get some data in, you can try out different algorithms (and see how difficult it is to build predictive systems!)<\/p>\n

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 <\/p>\n","protected":false},"excerpt":{"rendered":"

Weka is a GPL data mining tool written in Java, published by the University of Waikato. It includes an extensive series of pre-implemented machine learning algorithms, including well known classification and clustering algorithms. If you’ve ever been curious how Bayes Theorem works, this is a great tool to get up and running. Weka uses a … <\/p>\n

Continue reading “A brief introduction to Weka”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"footnotes":""},"categories":[5,6],"tags":[80,115,147,157,300,352,437,532,595],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t