Tutorial (7): Exercise 3

Limitations of Information Gain

The dataset:

Date	District	House Type	Income	Previous Customer	Outcome
3/10/03	Suburban	Detached	High	No	Nothing
14/9/03	Suburban	Detached	High	Responded	Nothing
2/4/02	Rural	Detached	High	No	Responded
18/1/03	Urban	Semi-detached	High	No	Responded
3/4/03	Urban	Semi-detached	Low	No	Responded
15/10/02	Urban	Semi-detached	Low	Responded	Nothing
15/10/02	Rural	Semi-detached	Low	Responded	Responded
2/3/01	Suburban	Terrace	High	No	Nothing
4/5/03	Suburban	Semi-detached	Low	No	Responded
2/1/03	Urban	Terrace	Low	No	Responded
3/10/03	Suburban	Terrace	Low	Responded	Responded
3/10/03	Rural	Terrace	High	Responded	Responded
8/4/03	Rural	Detached	Low	No	Responded
6/5/02	Urban	Terrace	High	Responded	Nothing

The Decision Tree: Interactively build it

• Click on the root node below and start building the tree.
• Non leaf nodes can be "pruned" once they have been chosen (by clicking on the node and selecting "prune node completely")
• The ratios on the branches indicate how well the chosen attribute at a node splits the remaining data based on the target attribute ('outcome').
• Click on any nodes to hilight the rows in the data table that the rule down to that node covers.
• At each node, the entropy of the data at that point in the tree will be given.
• Information gain (entropy reduction) is specified for each attribute.
• Reducing entropy to zero is a way of building a decision tree here.
  When no more nodes can be expanded, the tree has classified all the training data.
• Notice that the date attribute is calculated as having a high information gain.
• This would be used as the root node in algorithms such as ID3. It splits the data effectively, but is it a good classifier? What would happen if we tried to use such a tree for prediction?

root node