LSG vs RR- khudkiklm

                                          LSG vs RR

LSG and RR are two different regularization techniques used in machine learning to prevent overfitting and improve model performance.

LSG stands for Least Squares Gradient, and it is a type of regularization that is commonly used in linear regression models. It adds a penalty term to the cost function, which is proportional to the square of the weights of the model. The LSG regularization helps to shrink the weights of the model towards zero, which reduces the model's complexity and helps to prevent overfitting.

RR, on the other hand, stands for Ridge Regression, and it is also a type of regularization that is commonly used in linear regression models. It adds a penalty term to the cost function, which is proportional to the square of the magnitude of the weights of the model. The Ridge Regression regularization helps to reduce the variance of the model by shrinking the weights of the model towards zero, but it also preserves the original scale of the weights.

In summary, LSG and RR are both regularization techniques that help to prevent overfitting and improve model performance, but they differ in the way they penalize the weights of the model. LSG tends to shrink the weights towards zero, while RR tends to preserve the original scale of the weights while shrinking them towards zero. The choice between LSG and RR depends on the specific problem and the type of model being used.