Abstract :

With Probability Theory, Machine Learning Models Can Address Uncertain Situations, Handle Data-based Choices And Examine Results In A Statistical Way. This Paper Discusses How Probability Theory Plays A Major Role In Creating Machine Learning Algorithms, Especially In Supervised Learning, Unsupervised Learning, Bayesian Methods And Probabilistic Graphical Models. Both Studying The Underlying Theories And Looking At Practical Applications Prove That Using Probability Leads To Better Predictive Models And Generalization Based On Data. The Study Further Mentions Combining Probabilistic Reasoning With Deep Learning And Reinforcement Learning. Findings Suggest That Probabilistic Methods Help Make Models Clearer And More Effective, Mainly In Situations With Many Input Features To Analyze. The Findings Of This Research Improve Our Knowledge Of The Probabilistic Fundamentals That Guide The Workings Of Contemporary Machine Learning. Keywords— Probability Theory, Machine Learning, Bayesian Inference, Probabilistic Models, Statistical Learning, Predictive Modeling, Uncertainty Quantification.

Published:

25-9-2025

Issue:

Vol. 25 No. 9 (2025)

Page Nos:

403-410

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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