I understand that LASSO logistic regression is the best solution for this problem, however it can not be run on a incomplete dataset. So i imputed my missing data with MICE package in R and found that
I''m using the LASSO method, in the problem of text classification (sentiment classification). The features I''m using are mainly Ngrams (every N consecutive words) and I''m using the LASSO specifical...
I''ve been reading Elements of Statistical Learning, and I would like to know why the Lasso provides variable selection and ridge regression doesn''t. Both methods minimize the residual sum of squar...
I am working on a validity study about some measures of human walking and using a LASSO linear regression for variable selection. More precisely, the study aims to find the predictors of a gait par...
I''m currently studying Lasso regression to use it as a variable selection method. When I get a plot like above, shouldn''t X1 be the most important variable as it is the last variable to shrink to
Unlike LASSO and ridge regression, NNG requires an initial estimate that is then shrunk towards the origin. In the original paper, Breiman recommends the least-squares solution for the initial estimate
What do you mean by "When I go for a linear model with all variables (lambda.min variant)..."? Does that mean you are taking the predictors selected by LASSO and using them in a standard, non-penalized
Thank you, @FrankHarrell. For "regular regression on clusters of predictors" do you mean regress y on each group of variables separately? How would you aggregate the different regresions into a single
LASSO regression is a type of regression analysis in which both variable selection and regulization occurs simultaneously. This method uses a penalty which affects they value of coefficients of
Yes I run lasso 30 runs, and for each run, I order the absolute value of the regression in a ranking of 1 to 41 (41 predictors) to map it, but I was not certain if it is a good methodology. Not sure if I can perform
Wersja PDF zawiera pełny artykuł z odniesieniami źródłowymi. Idealna do druku i czytania offline.
