Using the type = "raw" option for the predict() function after repeated cross validation for logistic lasso regression returns empty vector

Question

I used the caret and glmnet pacakges to run a lasso logistic regression using repeated cross validation to select the optimized minimum lambda.

glmnet.obj <- train(outcome ~ .,
                     data = df.train,
                     method = "glmnet",
                     metric = "ROC",
                     family = "binomial",
                     trControl = trainControl(
                                          method = "repeatedcv",
                                          repeats = 10,
                                          number = 10,
                                          summaryFunction = twoClassSummary,
                                          classProbs = TRUE,
                                          savePredictions = "all",
                                          selectionFunction = "best"))

After that, I get the best lambda and alpha:

best_lambda<- get_best_result(glmnet.obj)$lambda 
best_alpha<- get_best_result(glmnet.obj)$alpha 

Then I obtain the predicted probabilities for the test set:

pred_prob<- predict(glmnet.obj,s=best_lambda, alpha=best_alpha, type="prob", newx = x.test)

and then to get the predicted classes, which I intend to use in ConfusionMatrix:

pred_class<-predict(glmnet.obj,s=best_lambda, alpha=best_alpha, type="raw",newx=x.test)

But when I just run pred_class it returns NULL.

What could I be missing here?

Answer 1

You need to use newdata = as opposed to newx= because when you do predict(glmnet.obj), it is calling predict.train on the caret object.

You did not provide one function, but I suppose it is rom this source:

get_best_result = function(caret_fit) {
  best = which(rownames(caret_fit$results) == rownames(caret_fit$bestTune))
  best_result = caret_fit$results[best, ]
  rownames(best_result) = NULL
  best_result
}

Using an example data

set.seed(111)
df = data.frame(outcome = factor(sample(c("y","n"),100,replace=TRUE)),
matrix(rnorm(1000),ncol=10))
colnames(df.train)[-1] = paste0("col",1:10)

df.train = df[1:70,]
x.test = df[71:100,]

And we run your model, then you can predict using the function:

pred_class<-predict(glmnet.obj,type="raw",newdata=x.test)

confusionMatrix(table(pred_class,x.test$outcome))
Confusion Matrix and Statistics

          
pred_class  n  y
         n  1  5
         y 11 13

The arguments for lambda = and newx= comes from glmnet, you can potentially use it on glmnet.obj$finalModel , but you need to convert the data into a matrix, for example:

predict(glmnet.obj$finalModel,s=best_lambda, alpha=best_alpha, 
type="class",newx=as.matrix(x.test[,-1]))