Brief Study of Classification Algorithms in Machine Learning

<p>The purpose of this study is to briefly learn the theory and implementation of three most commonly used Machine Learning algorithms: k-Nearest Neighbors (kNN), Decision Trees and Naïve Bayes. All these algorithms fall under the Classification algorithm category of Unsupervised Machine Learning. This paper is constructed structurally in explaining the working theory behind each algorithm and an implementation of a Machine Learning problem solved by each algorithm. KNN algorithm is designed using Euclidean distance measurement and Decision Trees make use of ID3 algorithm as a basis. We conclude the study by providing an overall picture of its strengths and weaknesses in solving different types of problems. Also a major point to note is that this paper is not a comparison between these three algorithms.</p>

Name thesis:degree_name

Master of Engineering (M.E.)

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Engineering

Year dc:date.available

2017

Author dc:creator

• Sankara Subbu, Ramesh

Contributors dc:contributor

• Bo Yuan

• Other Computer Engineering
• Other Electrical and Computer Engineering

Repository record dc:identifier

https://academicworks.cuny.edu/cc_etds_theses/679

OAI identifier oai:identifier

oai:academicworks.cuny.edu:cc_etds_theses-1680