Testing Of Hypothesis for Model Selection

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.