@jcblum
I am sorry I don't know your name so I will call you jcblumn for now.
Additional help: where to find a good laidback explanation of glmnet() package?
For instance, I want to understand what the message glmnet() delivers?
Maybe a source with an example, result, interpretation?
I have found several sources from stack overflow, Stanford website(https://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html)
but I still don't understand how it can be classifed as "Logistic Regression".
Or maybe we should all look at my model and help me understand the result.
> xfactors<model.matrix(Class ~ CUST_REGION_DESCR + cust_prog_level,data=down_train)
> x = as.matrix(data.frame(down_train$Sales,xfactors))
> cvfit = cv.glmnet(x, y=as.factor(down_train$Class), alpha=1, family="binomial")
> summary(cvfit)
Length Class Mode
lambda 58 none numeric
cvm 58 none numeric
cvsd 58 none numeric
cvup 58 none numeric
cvlo 58 none numeric
nzero 58 none numeric
name 1 none character
glmnet.fit 13 lognet list
lambda.min 1 none numeric
lambda.1se 1 none numeric
call 5 none call

I think "cv" stands for "cross validation". What does it do in the Logistic Regression Model?

I know the lm() package builds a linear regression model. Does glmnet() also build a regression model that helps to predict whether the predict "Class" responds to the Independent Variables on the right hand side?
glmmod = glmnet(x,y=as.factor(down_train$Class),alpha=1,family='binomial')
 What does "alpha = 1" mean?
Thanks all! @cderv