CS539 Machine Learning - Fall 2014 
Project 2 - Decision Trees

PROF. CAROLINA RUIZ 

DUE DATE: Tuesday, Sept 23rd 2014:

• Slides are due at 10:00 am (by email to Prof. Ruiz), and
• a hardcopy of the written report is due at 2:00 pm (beginning of class).

 

Project Assignment:

• Read Chapter 3 of the textbook about decision trees in great detail.

• THOROUGHLY READ AND FOLLOW THE PROJECT GUIDELINES. These guidelines contain detailed information about how to structure your project, and how to prepare your written and oral reports.

  *** You must use the Project 2 Template provided for your written report. *** (if you prefer not to use Word, you can copy and paste this format in a different editor as long as you respect the stated page structure and page limit.)

  • Machine Learning Technique(s): We will run experiments using J4.8 in Weka (given that J4.8 is able to handle numeric attributes and missing values directly, make sure to run some experiments with no pre-processing and some experiments with pre-processing, and compare your results); and the decision tree functions in R.

  • Dataset: In this project, we will use the dataset described below.

    The Diabetes 130-US hospitals for years 1999-2008 Data Set available from the Univ. of California Irvine (UCI) Data Repository.
    

    • Browse addional information provided about this dataset in the paper cited on the dataset website:

      Beata Strack, Jonathan P. DeShazo, Chris Gennings, Juan L. Olmo, Sebastian Ventura, Krzysztof J. Cios, and John N. Clore, .Impact of HbA1c Measurement on Hospital Readmission Rates: Analysis of 70,000 Clinical Database Patient Records,. BioMed Research International, vol. 2014, Article ID 781670, 11 pages, 2014. .

      In particular, carefully read the description of the attributes provided in Table 1 of this paper. Note that even though some attributes are codified using numeric values, they are nominal (not numeric) attributes. Use the type of each attribute (numeric or nominal) as provided in Table 1 in the remainder of this project.
    • Use "readmitted" as the target attribute.
    • Use the knowledge gained in Project 1 to handle and pre-process this dataset.

  • Performance Metric(s):
    • Use (1) classification accuracy, (2) size of the tree, and (3) readability of the tree, as separate measures to evaluate the "goodness" of your models.
    • Compare accuracies you obtain against those of benchmarking techniques as ZeroR and OneR over the same (sub-)set of data instances you used in the corresponding experiment.
    • Remember to experiment with pruning in J4.8 and R: Experiment with pre- and/or post-prunning of the decision tree in order to increase the classification accuracy and/or to reduce the size of the decision tree.

• Advanced Topic(s): Investigate in more depth (experimentally, theoretically, or both) a topic of your choice that is related to decision trees and that is not covered already in this project. This decision tree-related topic might be something that was described or mentioned in the textbook or in class, or that comes from your own research, or that is related to your interests. Just a few ideas are: The prune function in R; C4.5; C4.5 pruning methods (for trees or for rules); any of the additional tree classifiers in Weka: DecisionStump, LMT RandomForest, RandomTree, REPTree; meta-learning applied to decision trees (see Classifier -> Choose -> meta); any additional tree learning methods in R; an idea from a research paper that you find intriguing; ...

• Project 2 Grading Sheet