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Regularization and tuning of linear models are techniques used to improve the performance and generalization of linear regression models. Regularization methods such as L1 (Lasso) and L2 (Ridge) regularization are applied to prevent overfitting by penalizing large coefficients. Tuning involves selecting the appropriate regularization strength or hyperparameters through techniques like cross-validation. These methods help balance model complexity and predictive accuracy, making linear models more robust and reliable for real-world applications across various domains.
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Language: English
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