Your task for this project is to extend the ID3 classifier (provided in the Weka package) to support postpruning.
Use the UCI Machine Learning Repository ( http://www.ics.uci.edu/~mlearn/MLRepository.html ) Iris ( http://archive.ics.uci.edu/ml/index.php ) and Adult dataset for this tasks. You are welcome to try on other datasets, but the results you turn in should be based on these datasets.
The project report should contain the following:
1. Description of the method used (e.g., cost-based pruning).
2. Documentation for how to use your class (should probably inherit from weka.classifiers.trees.Id3).
3. Sample run and results.
4. Commentary: Does it work well (e.g., accuracy, efficiency)? What do you think are the advantages/disadvantages? If you were to do it again, what would you do differently?
Also turn in your code (obviously.)
Scoring will be based on:
Correctness of execution (1-2 points)
Quality of interface defined (1-2 points)
Quality of documentation (1-2 points)
Quality/readability of code (1 point)
Difficulty of pruning method used (1 point)
Demonstration of understanding of tradeoffs/issues (1 point)