Portfolio Assignment 4

Classification

Summary

Add a .html file to your portfolio titled Classification.

Purpose & Requirements

Demonstrate your knowledge of classifiers by describing how classifiers work in general, and how the classic Decision Tree, Rule-Based, Nearest-Neighbor, Bayesian and ANN algorithms work in a classification context. Your writing should include:

• An introduction that summarizes what we mean by "classification" and the typical classification process (training, testing, etc)
• Brief sections for each classifier algorithm (Decision Tree, Rule-Based, NN, Bayesian, and ANNs)
• Each section should include the algorithm's title, a brief but accurate description, perhaps an illustration, benefits and drawbacks of each, and common use cases (when might an ANN be the preferred strategy?)
• Provide example implementations (your code) of the ANN you implemented in Project 7.

This document should be equivalent to two or three double-spaced, typewritten pages in length. Be brief, but thorough -- the reader should have a good understanding about the various algorithms, and believe that you have a strong understanding of them.

Grading Criteria (250 points)

Your portfolio must contain a table of contents in index.html and have one link called Classification; and one page called classification.html that fulfills the requirements described above.