explain Regularization

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Regularization is a technique used to prevent overfitting in machine learning models.

Regularization adds a penalty term to the loss function to discourage complex models.
It helps to reduce the variance of the model and improve its generalization performance.
L1 and L2 regularization are commonly used techniques.
L1 regularization adds the absolute value of the coefficients to the loss function.
L2 regularization adds the square of the coefficients to the loss function.
The strength of regularization can be controlled using a hyperparameter.
Regularization is particularly useful when dealing with high-dimensional data.
Examples of regularization techniques include Ridge regression, Lasso regression, and Elastic Net.