Linear Algebra for Data Science
Regularization techniques are methods used in statistical modeling and machine learning to prevent overfitting by adding a penalty term to the loss function. These techniques help ensure that models generalize better to unseen data by discouraging overly complex models and promoting simplicity. By tuning model parameters, regularization can balance the trade-off between bias and variance, enhancing predictive performance in various applications.
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