- Tutorial (1): A simple decision tree
- Tutorial (2): Exercise 1
- Tutorial (3): Occam's Razor
- Tutorial (4): ID3
- Tutorial (5): Exercise 2
- Tutorial (6): Entropy Bias
- Tutorial (7): Exercise 3
- Tutorial (8): Other Splitting Criteria
- Tutorial (9): Exercise 4
- Tutorial (10): Advanced Topics
- Tutorial (11): Evaluating Decision Trees
- Tutorial (12): Exercise 5
- Tutorial (13): Overfitting
- Tutorial (14): Pruning
- Tutorial (15): Exercise 6
- Tutorial (16): Further Topics
- Tutorial (17): Conclusion
Tutorial (2): Exercise 1
Constructing a Simple Tree
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 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.
| Click here to begin |
As with all Decision Making
Beautifully detailed. Great