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Decision tree algorithm short Weka tutorial. Croce Danilo, Roberto Basili. Machine leanring for Web Mining. a.a. 2009-2010 Nov 14, 2016 The C4.5 algorithm for building decision trees is implemented in Weka as a classifier called J48. Classifiers, like filters, are organized in a hierarchy: J48 has the full name weka.classifiers.trees.J48. The classifier is shown in the text box next to the Choose button: It reads J48 –C 0.25 –M 2. Mar 2, 2015 Weka. -. Decision Trees. Page 2. Weka. • Waikato Environment for Knowledge Analysis Available at www.cs.waikato.ac.nz/ml/weka. Sep 22, 2013 The immediately following pages are taken from the Weka tutorial in the book .. with both IBk and the C4.5 decision tree learners, implemented in Weka as J48. This example illustrates the use of C4.5 (J48) classifier in WEKA. The sample data set used for this example, unless otherwise indicated, is the bank dataDecision tree algorithm short Weka tutorial Croce Danilo, Roberto Basili Machine leanring for Web Mining a.a Machine Learning: brief summary Example You Jan 31, 2016 Understand how a decision tree learning work. The article makes use of Java and Weka to help explain the basic concepts. Apr 7, 2016 Jul 25, 2016 After the tree is constructed, it is pruned in order to improve the model's ability to generalize to new data. Choose the decision tree algorithm: Click the “Choose” button and select “REPTree” under the “trees” group. Click on the name of the algorithm to review the algorithm configuration.