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 simple/insensitive model to …

Mar 19, 2023 · Regularization is a way of sacrificing the training loss value in order to improve some other facet of performance, a major example being to sacrifice the in-sample fit of a machine learning …

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 main interest is …

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 regularization (L1 or …

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 such …

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 of tree …

Mar 27, 2017 · The intuition behind regularization is that I have some vector, and I would like that vector to be "small" in some sense. How do you describe a vector's size? Well, you have choices: Do you …

Dec 13, 2019 · I keep reading this and intuitively I can see this but how does one go from L2 regularization to saying that this is a Gaussian Prior analytically? Same goes for saying L1 is …

If we select different values of the parameter λ λ, we could obtain solutions with different sparsity levels. Does it mean the regularization path is how to select the coordinate that could get faster …