One of the main advantages of decision trees is the possibility, for the users, to interactively build the prediction model. In this tutorial, we show how, using SIPINA and ORANGE, we build and manually modify a decision tree; especially we select the split attribute and
pruning the tree.
SIPINA is one of my old projects. It was very useful but it had some limitations, which have been rectified in TANAGRA: it was intended only for supervised learning; we cannot define and save the sequences of treatments in a diagram. Nevertheless, I use still this version for my courses, in particular for its functionalities in the interactive construction of decision trees.
SIPINA uses a graphical representation of the tree; ORANGE uses a standard treeview components. We will see that they propose very similar functionalities and provide the same results.
Keywords: decision tree, classification tree, interactive analysis, classififer assessment, orange
Tutorial: en_Tanagra_Interactive_Tree_Builder.pdf
Dataset: iris_tree.txt
References:
Orange - "Interactive tree builder"
