COSC-575 Project 5

COSC-575: Machine Learning

Project 5
Spring 2017

Due: Mon, May 1 @ 11:59 P.M.
10 points

Modify your implementation of DT and related classes from p3 so DT learns from weighted examples.
Implement Flach's Algorithm 11.1 as the executable Bagging. Bag DT. Don't post-prune. By default, Bagging should produce 10 classifiers. Predict by averaging.
Implement Flach's Algorithm 11.3 as the executable Boosting. Boost DT. Don't post-prune. By default, Boosting should produce 10 classifiers.

The implementations must follow sound principles of object-oriented Implement each learner as a single executable. No windows. No menus. No prompts. Just do it.

The logic of the implementation should be the same as that for the previous implementations. If the user runs a learner and specifies only a training set, then the program should evaluate using 10-fold cross-validation and output the results. Naturally, the user can use the -x switch to change the default. If the user provides the -p switch and a proportion, then the program conducts an evaluation using the hold-out method. Otherwise, if the user specifies both a training and testing set, then the program should build a model from the training set, evaluate it on the testing set, and output the results.

Instructions for Submission

In a file named HONOR, please include the statement:

In accordance with the class policies and Georgetown's Honor Code,
I certify that, with the exceptions of the class resources and those
items noted below, I have neither given nor received any assistance
on this project.

Include this file in your zip file submit.zip.

Submit p5 exactly like you submitted p4.

Plan B

If Autolab is down, upload your zip file to Blackboard.