Regularization
Regularization is a technique used in machine learning to prevent overfitting and improve the generalization of the model. It involves adding a penalty term to the loss function that the model is trying to optimize. This penalty term discourages the model from assigning too much importance to any one feature, and instead encourages it to use all the features to make predictions. There are different types of regularization techniques such as L1 regularization (Lasso), L2 regularization (Ridge), and Elastic Net regularization. L1 regularization adds a penalty term proportional to the absolute value of the coefficients, while L2 regularization adds a penalty term proportional to the square of the coefficients. Elastic Net regularization is a combination of both L1 and L2 regularization. Regularization is particularly useful when dealing with high-dimensional data, where the number of features is much larger than the number of samples.
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