L1 Regularization
L1 regularization, also known as Lasso regularization, is a technique used in machine learning and statistical models to prevent overfitting and improve the model's generalization performance. It adds a penalty term to the loss function of the model, which is proportional to the absolute value of the coefficients of the features. This penalty term encourages the model to select only the most important features and set the coefficients of the less important features to zero. L1 regularization is particularly useful when dealing with high-dimensional data, where the number of features is much larger than the number of samples. It can also be used for feature selection, as it automatically selects the most relevant features and discards the irrelevant ones.
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