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Regularization in machine learning is a technique used to prevent overfitting, where a model performs well on training data but poorly on unseen data. Regularization adds a penalty to the loss function used to train the model, discouraging overly complex models and promoting simpler, more generalizable ones.

Why Regularization Matters

Regularization works by adding an extra term to the loss function - a term that increases as the model complexity increases. For example, L1 and L2 are common regularization techniques that add the sum of the absolute values (L1) or the sum of the squares (L2) of the model coefficients to the loss function. This penalizes large coefficients, leading to simpler models that are less likely to overfit on the training data.


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