Exploring the Impact of Statistical Methods on Machine Learning Model Optimization: Bridging Theory with Practical Applications

Abstract

Statistics is integral to the design and improvement of machine learning models, impacting data preprocessing, pattern recognition, and predictive performance. This paper investigates the role of statistics in machine learning, examining how traditional statistical methods such as hypothesis testing, regression modeling, and Bayesian inference are used to develop, assess, and enhance machine learning models. We examine their role in model selection, feature selection, and model evaluation, particularly in improving model performance and preventing overfitting. Furthermore, this paper discusses the growing influence of statistics in contemporary machine learning pipelines, including new statistical approaches such as deep learning, ensemble methods and statistical learning theory. We provide case studies and examples from healthcare, finance, and image recognition to illustrate how these statistical techniques improve machine learning models.