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An Empirical Analysis of Overfitting Mitigation Techniques Using Regularized Linear Regression: A Case Study on Noise Data Modeling

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Ridge regression significantly reduces mean squared error, improving prediction accuracy by 7.3%.
When λ = 0.1, the Ridge model achieves the lowest MSE at 4.31 on the test set.
The study employs gradient descent for regularized regression, enhancing training efficiency and performance.
Regularization mechanisms can effectively mitigate overfitting and can apply to polynomial regression models.

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Empirical analysis reveals ridge regression reduces overfitting in linear models, improving accuracy.

Ren Yi (2025) studied this question.

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