Brief Study of Classification Algorithms in Machine Learning

Author

Ramesh Sankara Subbu, CUNY City CollegeFollow

Date of Award

2017

Document Type

Thesis

Department

Engineering

First Advisor

Bo Yuan

Abstract

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.

Recommended Citation

Sankara Subbu, Ramesh, "Brief Study of Classification Algorithms in Machine Learning" (2017). CUNY Academic Works.
https://academicworks.cuny.edu/cc_etds_theses/679