Enhancing Machine Learning Models: a Comparative Analysis of Approaches and Techniques

EasyChair Preprint 15490

Abstract

This paper explores advancements in Machine Learning (ML) models, focusing on comparing different techniques, including supervised and unsupervised learning methods. We present a systematic review of the most widely used algorithms, analyzing their effectiveness in various domains. Through mathematical modeling and experimental results, we assess how different ML methods handle real-world problems, particularly in terms of accuracy, efficiency, and scalability. Our findings reveal the strengths and weaknesses of different approaches, providing valuable insights for researchers and practitioners aiming to optimize ML model performance in diverse applications.

Keyphrases: Algorithms, analysis, machine learning, model

@booklet{EasyChair:15490,
  author    = {Maria Kin and Elvard John and Aarav Thakurani and Mehmmet Amin},
  title     = {Enhancing Machine Learning Models: a Comparative Analysis of Approaches and Techniques},
  howpublished = {EasyChair Preprint 15490},
  year      = {EasyChair, 2024}}