Classification using Logistic Regression and Support Vector Machines
Faculty Mentor
Xiuqin Bai
Document Type
Oral Presentation
Start Date
10-5-2023 9:30 AM
End Date
10-5-2023 9:50 AM
Location
PUB 317
Department
Mathematics
Abstract
Classification is a crucial tool in the fields of statistics and machine learning. The purpose of this research is to compare the classification results obtained through statistical methods and the support vector machine method (SVM). Logistic regression is a statistical method that uses probability theory to analyze binary variables or variables with more than two categories. SVM classification, on the other hand, involves selecting different kernel functions and hyper-parameters to determine the classification. By comparing the classifications of the same dataset, the ACME insurance dataset, we aim to identify any differences between logistic regression and SVM. The data used is from ACME insurance including personal attributes as well as the amount charged to an individual's health insurance. We will use each classification technique to identify individuals who we believe have incorrectly reported their smoking status which leads to lower monthly insurance payments.
Recommended Citation
Criswell, Ryan, "Classification using Logistic Regression and Support Vector Machines" (2023). 2023 Symposium. 1.
https://dc.ewu.edu/srcw_2023/res_2023/os1_2023/1
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Classification using Logistic Regression and Support Vector Machines
PUB 317
Classification is a crucial tool in the fields of statistics and machine learning. The purpose of this research is to compare the classification results obtained through statistical methods and the support vector machine method (SVM). Logistic regression is a statistical method that uses probability theory to analyze binary variables or variables with more than two categories. SVM classification, on the other hand, involves selecting different kernel functions and hyper-parameters to determine the classification. By comparing the classifications of the same dataset, the ACME insurance dataset, we aim to identify any differences between logistic regression and SVM. The data used is from ACME insurance including personal attributes as well as the amount charged to an individual's health insurance. We will use each classification technique to identify individuals who we believe have incorrectly reported their smoking status which leads to lower monthly insurance payments.
