Description: L1 regularization, also known as Lasso regularization, is a technique used in machine learning and statistics to prevent overfitting in predictive models. This technique adds a penalty to the loss function that is proportional to the absolute magnitude of the model coefficients. This means that as the model is trained, it seeks to minimize not only the prediction error but also the sum of the absolute values of the coefficients. As a result, L1 regularization can lead to some coefficients being reduced to zero, implying that certain features of the dataset may be eliminated from the model. This feature selection property is particularly useful in situations where there are a large number of variables, allowing the model to be more interpretable and efficient. L1 regularization is widely used in various applications, including linear regression, logistic regression, and other machine learning models, where model simplicity and interpretability are crucial. In the context of various machine learning libraries and frameworks, implementing L1 regularization is straightforward and can be applied to different types of models, thereby improving the model’s generalization to new data.
