Friday, May 3, 2013

Tutorial: basic decision trees in rapid minor





The purpose of this tutorial is to introduce how to create basic decision trees in rapid minor. I will use a default dataset in rapid minor, “Iris”, for the purposes of this tutorial.


       1)      In order to access this data set, click the processes tab to make sure you are in the correct window, then go to the repository and click on the repository where it says data and open the drop down menu to see the data set “Iris” as shown in the picture below.


 

      2) Click and drag the data set into the main processes window. Once the object representing the data set is in the window, clock the bump on the back of it that says out. A line should appear. Connect that line to the bump at the corner of the window, then hit run at the top of the screen so that we can go look into the results tab to get a view of the structure of this data set.




        3)     Below, we can see the structure of the data we intend to create the decision tree around. You will notice that there are four attributes which are numerical data types and one attribute is a nominal label.






     4)     Click the tab necessary to go back to the main processes window.  In the Operators menu Click open the following drop down menus in this order: Modeling, Tree induction, Decision Tree. Drag the decision tree icon into the main processes window and make the connections shown in the picture below. After you have the main processes window set up as picture below, click run and rapid miner will take you to the output automatically.






         5)      Below is the resulting output for this decision tree from rapid minor using the default parameters for the rapid minor decision tree. The trees root node (at the top of the tree) begins with the a3 node in order to make decisions for classification. The results yield that for values in a3 which are less than or equal to 2.45, that can be shown to fully belong to the group “iris-setosa”, as an example. As you go down the tree, you acquire more and more criteria for some classification.  For further instruction on the use of decision trees in rapid minor, visit the rapid miner website.















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