I saw few posts here regarding my issue. I went through with those but still i couldnt figure out what i did wrong.
Any help will be highly appreciated.
I fitted a LASSO logistic regression model using glmnet package and caret package (which is a wrapper for glmnet package) and i am getting different results .
Here is my code :
Using glment package ,
require(ISLR) require(glmnet) y <- Smarket$Direction x <- model.matrix(Direction ~ Lag1 + Lag2 + Lag3 + Lag4 + Volume, Smarket)[, -1] lasso.mod <- cv.glmnet(x, y, alpha=1,family="binomial",nfolds = 5, type.measure="class", lambda = seq(0.001,0.1,by = 0.001)) > lasso.mod$lambda.min  0.1
using caret package ,
require(caret) set.seed(123) fitControl1 <- trainControl(method = "cv",number = 5,savePredictions = T,returnResamp="all") modelFitlassocvintm1 <- train(Direction ~ Lag1 + Lag2 + Lag3 + Lag4 + Volume, data=Smarket, method = "glmnet", trControl = fitControl1, tuneGrid=expand.grid( .alpha=1, .lambda=seq(0.001,0.1,by = 0.001)), family="binomial") modelFitlassocvintm1$bestTune alpha lambda 26 1 0.026
as you can see, based on 5-fold cross validation i am getting different values for the tuning parameter
lambda. can any one help me to figure out what did i do wrong ?