Is regularization really ever used to reduce underfitting? In my experience, regularization is applied on a complex/sensitive model to reduce complexity/sensitvity, but never on a …

Mar 19, 2023 · Regularization - penalty for the cost function, L1 as Lasso & L2 as Ridge Cost/Loss Function - L1 as MAE (Mean Absolute Error) and L2 as MSE (Mean Square Error) …

Mar 13, 2015 · A common way to reduce overfitting in a machine learning algorithm is to use a regularization term that penalizes large weights (L2) or non-sparse weights (L1) etc. How can …

Is regularization a way to ensure regularity? i.e. capturing regularities? Why do ensembling methods like dropout, normalization methods all claim to be doing regularization?

Jul 24, 2017 · In what circumstances should one consider using regularization methods (ridge, lasso or least angles regression) instead of OLS? In case this helps steer the discussion, my …

Feb 17, 2019 · In xgboost (xgbtree), gamma is the tunning parameter to control the regularization. I understand what regularization means in xgblinear and logistic regression, but in the context …

Apr 6, 2025 · I'm reading [Ilya Loshchilov's work] [1] on decoupled weight decay and regularization. The big takeaway seems to be that weight decay and $L^2$ norm …

Oct 18, 2021 · I've noticed that some people argue that adding noise to training data equivalent to regularizing our predictor parameters. How is this the case? Some of the examples listed on …

Nov 29, 2015 · Note: I know that L1 has feature selection property. I am trying to understand which one to choose when feature selection is completely irrelevant. How to decide which …

Feb 15, 2017 · Regularization using methods such as Ridge, Lasso, ElasticNet is quite common for linear regression. I wanted to know the following: Are these methods applicable for logistic …