Integrating Statistical Techniques in Machine Learning Algorithms |
Author(s): |
| Yatin Singh , Chandigarh University; Dipanshu, Chandigarh University; Anuj Singh, Chandigarh University; Smile Katoch, Chandigarh University |
Keywords: |
| Machine Learning, Statistical Techniques, Hypothesis Testing, Regression Analysis, Model Validation, Generalization, Predictive Models, Multidisciplinary Approach |
Abstract |
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This paper has described one way of combining statistical techniques to improve accuracy, robust ness, and interpretability within the algorithms of machine learning. Practical problems with machine learning models are found in three common issues overfitting, under fitting, and data imbalance. Key statistical method regression analysis is done which establishes relationship between the variables. Hypothesis testing that test the valid assumptions. Bayesian inference that refines the prediction while looking at newly available data. It also discusses advanced techniques like feature selection and model validation to optimize the whole machine learning models. Examples of practical applications and case studies illustrate how statistical precision seeps into the everyday predictions and performance enhancement of machines. |
Other Details |
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Paper ID: IJSRDV12I110050 Published in: Volume : 12, Issue : 11 Publication Date: 01/02/2025 Page(s): 87-91 |
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