##Dependencies

1. numpy
2. matplotlib
3. pillow
4. scipy

The source code can be run under windows or linux with python 2.7+ and the libraries above.

##Directory structure

./
├── doc
│   └── report.pdf  (the report)
├── asset
│   ├── dataset.txt  (data set)
│   ├── learning-curve.png  (learning curve plot)
│   ├── pruned-tree.png  (visualization of the pruned decision tree)
│   └── tree.png  (visualization of the decision tree)
└── src
    ├── main.py  (generate the learning curve)
    ├── preprocess.py  (split the dataset and store them in json)
    ├── tree.py  (for building, pruning, drawing decision trees)
    └── util.py  (directory structure configurations)

##How to generate the results

Note: python scripts should be run under the src directory. All images will be placed under the asset directory.

1. Make sure the data set dataset.txt is placed under asset
2. To sample from the dataset, run python preprocess.py under src. The training set and the test set will be stored as training.json and testing.json under asset. The default sampling probability of the test set is 0.5. If you need to change it, for example, to 0.3, run python preprocess.py -p 0.3.
3. To plot the decision tree built with training.json and testing.json generated with preprocess.py, run python tree.py. The plots will be placed under asset named tree.png and pruned-tree.png
4. To generate the learning curve, run python main.py. The plot will be placed under asset named learning-curve.png

##About

• Github repository
• Time: Dec. 2014