RT Avi Chawla (@_avichawla)Few people know this about L2 regularization:(Hint: it is NOT just a regularization technique)Most models intend to use L2 Regularization for just one thing:↳ Reduce overfitting.However, L2 regularization is a great remedy for multicollinearity.Multicollinearity arises when:→ Two (or more) features are highly correlated, OR,→ Two (or more) features can predict another feature.To understand how L2 regularization addresses multicollinearity, consider a dataset with two features and ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/gly3mHfP4WAvi Chawla (@_avichawla):Few people know this about L2 regularization:(Hint: it is NOT just a regularization technique)Most models intend to use L2 Regularization for just one thing:↳ Reduce overfitting.However, L2 regularization is a great remedy for multicollinearity.Multicollinearity arises https://t.co/T4Q35XSrYM ...

Few people know this about L2 regularization:(Hint: it is NOT just a regularization technique)Most models intend to use L2 Regularization for just one thing:↳ Reduce overfitting.However, L2 regularization is a great remedy for multicollinearity.Multicollinearity arises when:→ Two (or more) features are highly correlated, OR,→ Two (or more) features can predict another feature.To understand how L2 regularization addresses multicollinearity, consider a dataset with two features and a dependent variable (y):→ ...