diff --git a/ccn2019-elasticnet.R b/ccn2019-elasticnet.R
new file mode 100644
index 0000000..7eea931
--- /dev/null
+++ b/ccn2019-elasticnet.R
@@ -0,0 +1,52 @@
+library(tidyverse)
+library(caret)
+library(here)
+library(inspectdf)
+library(glmnet)
+library(ROSE)
+
+rm(seqs)
+load(here("notebooks/data/nback_seqs.Rd"))
+seqs <- seqs %>% drop_na(rt, correct, tl,sl)
+
+f <- correct ~ n + stimulus + stimulus_type + t + s + v + l + vl + sl + tl + ul + ll
+
+set.seed(654321)
+
+train.indices <- createDataPartition(seqs[[toString(f[[2]])]], p = .8, list =FALSE)
+
+seqs.train.balanced <- seqs[train.indices,]
+seqs.train <- ROSE(f, data = seqs.train.balanced, N=100)$data
+
+seqs.train.x <- model.matrix(f, seqs.train)[,-1]
+seqs.train.y <- seqs.train[[toString(f[[2]])]]
+
+seqs.test  <- seqs[-train.indices,]
+seqs.test.x <-  model.matrix(f, seqs.test)[,-1]
+seqs.test.observed_y <- seqs.test[[toString(f[[2]])]]
+
+
+set.seed(10)
+
+ctrl <- trainControl(
+  method = "cv",
+  number = 10,
+  classProbs = T,
+  summaryFunction = twoClassSummary
+)
+
+grid <- expand.grid(
+  alpha = seq(0,1,length=10),
+  lambda = seq(0.0001, 0.1, length=10)
+)
+
+model <- train(seqs.train.x, seqs.train.y,
+               method = "glmnet",
+               preProcess = c("nzv","center","scale"), # or c("knnImpute"),
+               tuneGrid = grid,
+               trControl = ctrl)
+
+model
+plot(varImp(model, useModel = F))
+plot(model)
+max(model$results$ROC)

diff --git a/ccn2019-elasticnet.R b/ccn2019-elasticnet.R
new file mode 100644
index 0000000..7eea931
--- /dev/null
+++ b/ccn2019-elasticnet.R
@@ -0,0 +1,52 @@
+library(tidyverse)
+library(caret)
+library(here)
+library(inspectdf)
+library(glmnet)
+library(ROSE)
+
+rm(seqs)
+load(here("notebooks/data/nback_seqs.Rd"))
+seqs <- seqs %>% drop_na(rt, correct, tl,sl)
+
+f <- correct ~ n + stimulus + stimulus_type + t + s + v + l + vl + sl + tl + ul + ll
+
+set.seed(654321)
+
+train.indices <- createDataPartition(seqs[[toString(f[[2]])]], p = .8, list =FALSE)
+
+seqs.train.balanced <- seqs[train.indices,]
+seqs.train <- ROSE(f, data = seqs.train.balanced, N=100)$data
+
+seqs.train.x <- model.matrix(f, seqs.train)[,-1]
+seqs.train.y <- seqs.train[[toString(f[[2]])]]
+
+seqs.test  <- seqs[-train.indices,]
+seqs.test.x <-  model.matrix(f, seqs.test)[,-1]
+seqs.test.observed_y <- seqs.test[[toString(f[[2]])]]
+
+
+set.seed(10)
+
+ctrl <- trainControl(
+  method = "cv",
+  number = 10,
+  classProbs = T,
+  summaryFunction = twoClassSummary
+)
+
+grid <- expand.grid(
+  alpha = seq(0,1,length=10),
+  lambda = seq(0.0001, 0.1, length=10)
+)
+
+model <- train(seqs.train.x, seqs.train.y,
+               method = "glmnet",
+               preProcess = c("nzv","center","scale"), # or c("knnImpute"),
+               tuneGrid = grid,
+               trControl = ctrl)
+
+model
+plot(varImp(model, useModel = F))
+plot(model)
+max(model$results$ROC)
diff --git a/ccn2019-featsel.R b/ccn2019-featsel.R
index d59cd52..ae5dc98 100644
--- a/ccn2019-featsel.R
+++ b/ccn2019-featsel.R
@@ -26,14 +26,13 @@
 seqs.test.observed_y <- seqs.test[[toString(f[[2]])]]
 
 
-set.seed(10)
-
 ctrl <- rfeControl(functions = lrFuncs,
                    method = "cv",
-                   number = 5,
+                   number = 10,
                    verbose = T)
 
 rmProfile <- rfe(seqs.train.x, seqs.train.y,
+                 preProcess = c("nzv"),
                  rfeControl = ctrl)
 
 rmProfile