# Tagged Questions

A regularization method for regression models that shrinks coefficients towards zero, making some of them equal to zero. Thus lasso performs feature selection.

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### Things that I am not sure about “LASSO” regression method

I have read the chapters that are related to "LASSO" regression in: The elements of statistical learning (Tibshirani et al.) Statistical Learning with Sparsity: The Lasso and Generalizations. (...
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### Random forest and LASSO regression both give different variable importances

I have a dataset with 163 observations (all countries in the world with population > 1000000) and 290 variables related to their disease burden and performance. Because there are more variables than ...
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### Lasso logistic regression with GLMNET and fixed effects

I have a pretty general question. Suppose one collected data on investments in companies. Further, one wants to find out if some investors are better than others based on investment success (1/0 | ...
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### How do Shrinkage Methods change flexibility of a model?

While working through An Introduction to Statistical Learning, I had difficulty clarifying how flexibility relates to Ridge Regression and Lasso. I recognize that both impose penalties on the ...
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### How to use LassoCV to obtain most informative independent variables by setting weights of others to zero [closed]

I want to use the lassoCV function from scikit package. In total I've 8000 data. All of these 8000 points have labels. Up to today, I've always used 3 seperated ...
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### Challenges in interpretation of variable selection from LASSO and OLS [duplicate]

I work as a consultant and I am often faced with variable selection and prediction problems. For my clients, I run OLS and I am recently pushing for penalized methods which can handle variable ...
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### sparsity using lasso [closed]

MAtlab code X = randn(5,3); r = [0;2;0;]; Y = Xr + randn(5,1).1; B = lasso(X,Y); B(:,25) this is my code and i get the following output 0 1.3937 ...
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### glmnet for mixed models?

I perfom a lasso logistic regression using glmnet and want to account for fixed and random effects. I found R packages that can fit mixed models, e.g. glmmLasso and glmmboot. However, is it possible ...
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### Can LARS or Coordinate Descent select features that are marginally uncorrelated with the response?

I could construct a response Y the following way: Given $\left\lbrace X_k \right\rbrace_{k=1}^p$, and the regression model $$Y = \sum_{i = 1}^p X_i - \rho p \beta_{p+1}X_{p+1} + \varepsilon,$$ if ...
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### Help with hierNet package in R [closed]

I have been having some trouble using the hierNet package in R to run a logistic LASSO. I keep receiving this error message after I try to run this line, ...
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### Update rule for multi-task lasso used in scikit-learn

The document of scikit-learn said it use coordinate descent for training multi-task lasso. I have tried to derive the update rule but its too hard for me. Can you show me what is update rule for multi-...
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### Computational complexity of the lasso (lars vs coordinate descent)

The lasso can be computed with the LARS or Coordinate Descent algorithm. What is their computational complexity and when one is quicker than the other?
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### LASSO and compatibility constant

I am new on this web-site and coming from the field of economics (although interested in High Dimensional Statistics), I am reading Statistics for High Dimensional Data of Bühlmann and Van De Geer. I ...
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### Quick question on performance of lasso logisitc model

I performed a lasso logistic regression on two modles. One model contains only the control variables. The other model contains controls+linguistic measures. When I search for the optimum lambda ...