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Decision Tree Project

Aaron Gonzales

agonzales@cs.unm.edu

2015-02-08

Builds a decision tree classifier for the UCI promoter dataset. Report can be found here

REQUIREMENTS:

There are several dependencies:

I believe all of them may be installed with PIP or easy_install:

pip install networkx

or

easy_install -U networkx

Installing

You may download the repositiory from this link https://github.com/xysmas/decision_trees/archive/master.zip or just unzip the attached file:

unzip agonzales_decision_tree.zip

In the created directory, there will be this README.md file (can be opened with any real text editor), a data directory, and a src directory navigate to the src directory and you may run it from there

Running

The program may be ran like this:

usage: main.py [-h] -t TRAIN -v VALIDATION [--ipython] -x CONFIDENCE

Implements the classic ID3 algorithm for classifying a set of dna promoters.

optional arguments:
  -h, --help            show this help message and exit
  -t TRAIN, --train TRAIN
                        the data on which you wish to train e.g.
                        "../data/training.txt"
  -v VALIDATION, --validation VALIDATION
                        the validation data
  --ipython             this is an ipython session and we want to draw the
                        figs, not save them
  -x CONFIDENCE, --confidence CONFIDENCE
                        threshold confidence level for growing the decision
                        tree. Can either be (0, 95, 99)

For example, running and specifying a 95% confidence level:

python main.py --train ../data/training.txt --validation ../data/validation.txt --confidence 95

and the program will write several plots to your current directory while displaying minor output.