I am running the following (truncated) code using glmnet in R # do a lot of things to create the design matrix called x.design > glmnet(x.design, y, thresh=1e-11) where x.design is a n x p d...

Fused Lasso (Tibshirani et al, 2005) encourages sparsity of the coefficients and also sparsity of their differences. This is the formula for the loss function and regularization: The first term ...

I want to perform penalty selection for the LASSO algorithm and predict outcomes using tidymodels. I will use the Boston housing dataset to illustrate the problem. library(tidymodels) library(tidyv...

I am performing lasso regression in R using glmnet package: fit.lasso <- glmnet(x,y) plot(fit.lasso,xvar="lambda",label=TRUE) Then using cross-validation: cv.lasso=cv.glmnet(x,y) plot(cv...

The glmnet package uses a range of LASSO tuning parameters lambda scaled from the maximal lambda_max under which no predictors are selected. I want to find out how glmnet computes this lambda_max v...

I'm having an issue with glmnet in that I keep getting the error message "Error in elnet(x, is.sparse, ix, jx, y, weights, offset, type.gaussian, : NA/NaN/Inf in foreign function call (arg 5) In ...

Here is my code: library(MASS) library(caret) df <- Boston set.seed(3721) cv.10.folds <- createFolds(df$medv, k = 10) lasso_grid <- expand.grid(fraction=c(1,0.1,0.01,0.001)) lasso <- t...

Main issue: Why coefficients of Lasso regression are not shrunk to zero with minimization done by scipy.minimize? I am trying to create Lasso model, using scipy.minimize. However, it is working onl...

I'm trying to analyze a high dimensional data set (31 variables, 1100 observations) through a penalized vector autoregression. Since I'm using the techniques introduced by Diebold et. al (2019) to ...

The scikit-learn package provides the functions Lasso() and LassoCV() but no option to fit a logistic function instead of a linear one...How to perform logistic lasso in python?

I'm using scikit-learn's LassoCV function. During cross-validation, what scoring metric is being used by default? I would like cross-validation to be based on "Mean squared error regression loss"...

I am trying to conduct nested resampling with 10 CVs for the inner and 10 CVs for the outer loop using regr.cvglment. Mlr provides the code using a wrapper function (https://mlr-org.github.io/mlr/a...

I've got a dataset with 1000 observations and 76 variables, about twenty of which are categorical. I want to use LASSO on this entire data set. I know that having factor variables doesn't really wo...

I expect LASSO with no penalization ($\lambda=0$) to yield the same (or very similar) coefficient estimates as an OLS fit. However, I get different coefficient estimates in R putting the same data ...

Does anybody know how cv.glmnet (in R's glmnet) or LassoCV (scikit-learn) chooses a sequence of regularization constants (lambdas), which they use in cross-validation? Thank you very much!

I would love to use a linear LASSO regression within statsmodels, so to be able to use the 'formula' notation for writing the model, that would save me quite some coding time when working with many...

I'm trying to reproduce the following R results in Python. In this particular case the R predictive skill is lower than the Python skill, but this is usually not the case in my experience (hence th...

I am having a problem to run glmnet lasso with a wide data set. My data has N=50, but p > 49000, all factors. So to run glmnet i have to create a model.matrix, BUT i just run out of memory when i c...

I want to perform model selection among ~150 fixed-effect and 7 random-effect variables, on a set of 360 observations. I decided to use the Lasso procedure for mixed models, with the glmmLasso. I d...

How does glmnet in the R package 'glmnet' handle NA values? Or can it not tolerate NA values?

I need to call sklearn function on matlab. I have heard that there are some problems in calling numpy function and sklearn is based on numpy. Is there any guide about that?

I am having an hard time in getting the model estimated by the R package lars for my data. For example I create a fake dataset x and corresponding values y like this: x = cbind(runif(100),rnorm(1...

I am attempting to carry out lasso regression using the lars package but can not seem to get the lars bit to work. I have inputted code: diabetes<-read.table("diabetes.txt", header=TRUE) diabet...