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What is Regularization? What kind of problems does regularization solve?

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A regularization is a form of regression, which constrains/ regularizes or shrinks the coefficient estimates towards zero. In other words, it discourages learning a more complex or flexible model to avoid the risk of overfitting. It reduces the variance of the model, without a substantial increase in its bias.

Regularization is used to address overfitting problems as it penalizes the loss function by adding a multiple of an L1 (LASSO) or an L2 (Ridge) norm of weights vector w.

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