Lab 10: Categorising with Decision Trees

1. Here is the lense data from the UCI benchmark, and the description if you're interested.
2. Make a decision tree the correctly categorises the first four items. (The first four features are the inputs, and the last is the category.)
3. Allow the user to put in the item they're trying to categorise by inputing the 4 features; output the category.
4. If you'd like make a complete tree.
5. Use the C4.5 algorithm to make a good decision tree.
6. Implement the C4.5 algorithm to automatically learn the decision tree.
7. Split the data in half (say odds and evens). Learn the tree from the first half and categorise the second half. How well do you do?
8. Now do the second fold.