Limitations of Information Gain
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?