# Why all coeficents of features of model are zero while I have high deviance using glmnet?

I'm using gmlnet to learn lasso regression model.

model<-cv.glmnet(x, y, alpha=1, nfolds=10,parallel= TRUE)

when I learn model and look at the model it's like this :

Df     %Dev   Lambda

[79,] 411 0.766800 0.003736
[80,] 421 0.773000 0.003566
[81,] 433 0.779200 0.003404
[82,] 438 0.785000 0.003249
[83,] 444 0.791200 0.003102
[84,] 452 0.796500 0.002961
[85,] 453 0.802000 0.002826
[86,] 455 0.807600 0.002698
[87,] 457 0.812700 0.002575
[88,] 462 0.817700 0.002458
[89,] 467 0.822400 0.002346
[90,] 473 0.827000 0.002240
[91,] 478 0.831400 0.002138
[92,] 478 0.836100 0.002041
[93,] 484 0.840400 0.001948
[94,] 491 0.844600 0.001859
[95,] 498 0.848700 0.001775
[96,] 504 0.852800 0.001694
[97,] 504 0.856700 0.001617
[98,] 511 0.860100 0.001544
[99,] 516 0.863300 0.001474
[100,] 515 0.866500 0.001407


but when I look at the coefficients of model, all are zero. I just have intercept.

How is it possible I have high deviance, but no features has non-zero coefficient ?

When you use coef(model) to print coefficients of model after cross validation, the default returns the best model in a sequence of models,which corresponds to the model\$lambda.min/lse,this value may be very high in your first few lines of list that you haven't shown.