Linear Algebra and Differential Equations
Regularization techniques are methods used in statistical modeling and machine learning to prevent overfitting by introducing additional information or constraints into the model. By adding a penalty for complexity, these techniques help balance the fit of the model to the training data while maintaining its ability to generalize to unseen data. This approach is crucial when dealing with least squares approximations, as it can improve the robustness and reliability of the estimated parameters.
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