Using Entropy to Construct a Decision Tree

The dataset:

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 then specified for each attribute.
• Reducing entropy to zero is a way of building a decision tree here ( as the ID3 algorithm does).
  When no more nodes can be expanded, the tree has classified all the training data.