Data Science Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Preface Data Science Introducing R R Constructs R Tasks R Strings R Read, Write, and Create Data Template Data Exploration Data Wrangling Data Visualisation Statistics ML Template ML Scenarios ML Activities ML Applications ML Algorithms Cluster Analysis Decision Trees Computer Vision Graph Data Privacy Literate Data Science Coding with Style Resources Bibliography Index

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Rattle View Decision Tree

Click the Draw button to display the decision tree. A visual representation is often simpler to understand.

For a classification tree as we have here colour is used to differentiate the predicted (majority) class for each node. For our example the class/decision No is green and Yes is blue. The intensity of the colour indicates the strength of the prediction which is proportional to the percentage of the majority class within that node.

The root node (node number 1) has 84% No and 16% Yes and so is reported as a No decision or class. That is, in the absence of any other information, we predict that it will not rain tomorrow, and expect that prediction to be 84% correct.