Statistical Inference in Machine Learning Bridging Probability and Data Science

Abstract

Model parameters emerge from the process while uncertainty measurement and hypothesis tests are its essential outputs which machine learning algorithms use to draw meaningful data conclusions. The paper explores statistical inference role in machine learning while examining inference methods between probability theory and machine learning along with a detailed approach for connecting these domains. The paper examines maximum likelihood estimation (MLE) along with Bayesian inference and frequentist techniques as main approaches in statistical inference. The paper demonstrates ways to use these methods in combination with standard machine learning algorithms because it improves both model performance and decision systems. The paper introduces an extensive methodology that uses statistical inference approaches in machine learning and presents mathematical statements together with their effects on predictive and evaluating models and generalization.