Abstract Linear Algebra I
L2 regularization, also known as Ridge regression, is a technique used to prevent overfitting in machine learning models by adding a penalty term to the loss function based on the square of the coefficients. This method helps to shrink the weights of the model towards zero, thereby simplifying the model and making it more generalizable to unseen data. By incorporating L2 regularization, practitioners can achieve better performance in data analysis and machine learning applications, particularly when working with high-dimensional datasets or when the number of features exceeds the number of observations.
congrats on reading the definition of l2 regularization. now let's actually learn it.