Testing Of Hypothesis for Model Selection

Author(s)	R.Srilatha, Ch.Shashi Kumar, A. Ritheesh Reddy, R. Nihesh, K. Pavan Kumar, Para Rajesh
Country	India
Abstract	This paper examines the application of hypothesis testing in machine learning model selection, focusing on distinguishing between statistically significant performance differences and random variations. We demonstrate how statistical tests such as t-tests and ANOVA can be effectively combined with traditional evaluation metrics including accuracy, F1-score, and precision to validate model performance. This integration, along with cross-validation techniques, helps ensure model generalization while mitigating overfitting risks.
Keywords	Hypothesis Testing, Model Selection, Machine Learning, Statistical Significance, Cross-Validation, Model Performance, Accuracy, F1-Score, Statistical Tests, Decision Trees, Support Vector Machines, Neural Networks, Multiple Comparisons, Type I Error.
Field	Mathematics > Statistics
Published In	Volume 7, Issue 2, March-April 2025
Published On	2025-03-17
DOI	https://doi.org/10.36948/ijfmr.2025.v07i02.38155
Short DOI	https://doi.org/g895g4

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