Tutorial (15): Exercise 6

Decision Trees tutorial > Exercise 6:

Pruning

The dataset:

District	House Type	Income	Previous Customer	Outcome
Suburban	Detached	High	No	Nothing
Suburban	Detached	High	Yes	Nothing
Rural	Detached	High	No	Responded
Urban	Semi-detached	High	No	Responded
Urban	Semi-detached	Low	No	Responded
Urban	Semi-detached	Low	Yes	Nothing
Rural	Semi-detached	Low	Yes	Responded
Suburban	Terrace	High	No	Nothing
Suburban	Semi-detached	Low	No	Responded
Urban	Terrace	Low	No	Responded
Suburban	Terrace	Low	Yes	Responded
Rural	Terrace	High	Yes	Responded
Rural	Detached	Low	No	Responded
Urban	Terrace	High	Yes	Nothing

The Holdout (validation) data

Select rows by clicking to move data instances (rows) between tables

District

House Type

Income

Previous Customer

Outcome

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.
• Move data into the testing set from the training set (by clicking) and then contsruct a tree. Ideally a testing set should have 33% or less of the training data (about 3 or 4 instances here).
• Prune branches by replacing them with a single leaf node in order to improve combined classification accuracy.

root node

Classification Errors (Totals)

	Correct	Incorrect
Training set
Validation set