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Do we standardize dummy variables?
For example, many people don’t like to standardize dummy variables, which only have values of 0 and 1, because a “one standard deviation increase” isn’t something that could actually happen with such a variable. Ergo, you might want to leave the dummy variables unstandardized while standardizing continuous X variables.
What is a penalized regression?
Penalized regression methods keep all the predictor variables in the model but constrain (regularize) the regression coefficients by shrinking them toward zero. If the amount of shrinkage is large enough, these methods can also perform variable selection by shrinking some coefficients to zero.
How are categorical predictors standardized in penalized regression?
In penalized/regularized regression (lasso, ridge, etc.) the predictors are typically standardized to be centered at 0 and often to have variance 1. Are categorical predictors treated differently. If so, why?
Why are penalized / regularized regressions not suitable for interpretation?
Many times, penalized/regularized regressions are not suitable for interpretation, because you are introducing a bias in the coefficients. Usually when you use such models, you are interested in the predictions, not in doing a counterfactual analysis. Standardizations are useful because they make the problem numerically more stable.
What are the consequences of using the same standardization?
Closed 6 years ago. In penalized/regularized regression (lasso, ridge, etc.) the predictors are typically standardized to be centered at 0 and often to have variance 1. Are categorical predictors treated differently. If so, why? What are the consequences of using the same standardization?
When do you use binary logistic regression for?
Binary logistic regression is useful where the dependent variable is dichotomous (e.g., succeed/fail, live/die, graduate/dropout, vote for A or B). For example, we may be interested in predicting the likelihood that a