Ensemble Classification: An Analysis of the Random Forest Model

Author

Jarod KornFollow

College

Buchtel College of Arts and Sciences

Date of Last Revision

2024-06-04 07:21:35

Major

Statistics

Honors Course

STAT 498

Number of Credits

2

Degree Name

Bachelor of Science

Date of Expected Graduation

Spring 2024

Abstract

The random forest model proposed by Dr. Leo Breiman in 2001 is an ensemble machine learning method for classification prediction and regression. In the following paper, we will conduct an analysis on the random forest model with a focus on how the model works, how it is applied in software, and how it performs on a set of data. To fully understand the model, we will introduce the concept of decision trees, give a summary of the CART model, explain in detail how the random forest model operates, discuss how the model is implemented in software, demonstrate the model by applying it to a set of data, evaluate the performance of the model, and finally compare the model and its performance to the simpler CART model. By the end, a reader should have an introductory understanding of decision tree methodology, be knowledgeable about the mechanics of both the CART and random forest models, an understand both the benefits and hindrances of the random forest model.

Research Sponsor

Richard Einsporn

First Reader

Nao Mimoto

Second Reader

Mark Fridline

Honors Faculty Advisor

Nao Mimoto

Proprietary and/or Confidential Information

No

Recommended Citation

Korn, Jarod, "Ensemble Classification: An Analysis of the Random Forest Model" (2024). Williams Honors College, Honors Research Projects. 1806.
https://ideaexchange.uakron.edu/honors_research_projects/1806