Three Studies on the Practical Use of Machine Learning Techniques in Analytics
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Authors
Ying, Cecilia
Date
2025-05-05
Type
thesis
Language
eng
Keyword
Text Analytics , Machine Learning , Large Language Models , Automated Credit Assessment , ML Model Bias and Model Correction
Alternative Title
Abstract
The thesis investigates the application and societal implications of advanced machine learning techniques in data analytics through three empirical studies. The first study evaluates large language models (LLMs) in conversation analytics, finding that LLM-based configurations significantly outperform traditional methods in topic extraction tasks. The second study examines the impact of data sparsity on automated consumer credit assessments using machine learning algorithms, highlighting risks to borrowers who lack a substantial financial history. The third study introduces a post-processing technique, Subgroup Threshold Optimization (STO), to address performance disparities identified in the second study. This technique strikes a balance between fairness and performance, providing insights into the trade-offs between profit and fairness. The research aims to guide the responsible implementation of machine learning technologies, providing best-practice guidelines for practitioners.
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Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada
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This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
Attribution-NonCommercial-NoDerivatives 4.0 International
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
Attribution-NonCommercial-NoDerivatives 4.0 International