Abstract Linear Algebra II
Regularization techniques are methods used in statistical modeling and machine learning to prevent overfitting by adding a penalty term to the loss function. By constraining the model complexity, these techniques help to ensure that a model generalizes well to unseen data, improving its predictive performance. They are particularly important in contexts where models can become excessively complex, often leading to poor generalization.
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