query: Why we only use L1 and L2 in penalized regression
positive_passages.docid: 156700
positive_passages.text: Why do we only see $L_1$ and $L_2$ regularization but not other norms?
negative_passages.docid: 25322
negative_passages.text: Regression in $p>n$ setting: how to choose regularization method (Lasso, PLS, PCR, ridge)?
negative_passages.docid: 108556
negative_passages.text: Standardization vs. Normalization for Lasso/Ridge Regression
negative_passages.docid: 1348
negative_passages.text: Is there a difference between 'controlling for' and 'ignoring' other variables in multiple regression?
negative_passages.docid: 104131
negative_passages.text: LASSO: selection of penalty term: "one-standard-error" rule
negative_passages.docid: 85976
negative_passages.text: Implementing linear regression with standardization
negative_passages.docid: 29411
negative_passages.text: Why P>0.5 cutoff is not "optimal" for logistic regression?
negative_passages.docid: 17773
negative_passages.text: Why is polynomial regression considered a special case of multiple linear regression?
negative_passages.docid: 102544
negative_passages.text: Why should one use EM vs. say, Gradient Descent with MLE?
negative_passages.docid: 22668
negative_passages.text: Linear vs. nonlinear regression
negative_passages.docid: 97758
negative_passages.text: Is using correlation matrix to select predictors for regression correct?
negative_passages.docid: 98605
negative_passages.text: Ridge/Lasso regression negative Lambda
negative_passages.docid: 26034
negative_passages.text: Choosing variables to include in a multiple linear regression model
negative_passages.docid: 76906
negative_passages.text: Penalized methods for categorical data: combining levels in a factor
negative_passages.docid: 13077
negative_passages.text: Graphical interpretation of LASSO
negative_passages.docid: 58249
negative_passages.text: Why do we use a Least Squares fit?
negative_passages.docid: 94847
negative_passages.text: Why is GLM different than an LM with transformed variable