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 Build Tree

We can simply click the Execute button to build our first decision tree, using all of the default settings. Once finished, as in the figure above, the time taken to build the tree is reported in the status bar at the bottom of the window. A summary of the tree is presented in the text view panel. A classification model is built using rpart().

The number of observations from which the model was built is reported. This is 70% of the observations available in the weather dataset, representing the training dataset. We again note that the weather dataset is quite a tiny dataset in the context of data mining, but suitable for our purposes here.

The actual decision tree is then presented in its textual form.