Communicating Results

• Some models conducive to clear visualisation, e.g. decision-tree model can be ploted with {rpart.plot}
• This sets out the fitted algorithmic choices and their effect on the predicted outputs

Fig 11.8: Decision tree for Ischaemic Stroke

• Random forests can show variable importance plots
• These show the ranked importance of each predictive element
• Low ranking elements can be dropped if little effect on the model

Fig 11.9: Variable Importance for Ischaemic Stroke

• Neither visualisation makes a causal claim, but can often give clues to make inferences.