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

CLICK HERE TO VISIT THE UPDATED SURVIVAL GUIDE

RPart Plot Favourite

prp(model, type=2, extra=104, nn=TRUE, fallen.leaves=TRUE,
    faclen=0, varlen=0, shadow.col="grey", branch.lty=3)

This is a plot that I find particularly useful, neat, and informative, particularly for classification models.

The leaf nodes are each labelled with the predicted class. They are neatly lined up at the bottom of the figure (fallen.leaves=TRUE), to visually reinforce the structure. We can see the straight lines from the top to the bottom which lead to decisions quickly, whilst the more complex paths need quite a bit more information in order to make a decision.

Each node includes the probability for each class, and the percentage of observations associated with the node (extra=104). The node numbers are included (nn=TRUE) so we can cross reference each node to the text decision tree, or other decision tree plots, or a rule set generated from the decision tree.

Using a dotted line type (branch.lty=3) removes some of the focus from the heavy lines and back to the nodes, whilst still clearly identifying the links. The grey shadow is an optional nicety.