xgbTuneGrid = expand.grid(
      nrounds = 10,
      eta = c(0.001, 0.1),
      max_depth =  c(2,6),
      gamma = 0,
      colsample_bytree=0.6,
      min_child_weight=1,
      subsample = 0.75
    )
    ctrl <- trainControl(method = "repeatedcv",number = 2,summaryFunction=twoClassSummary,classProbs=TRUE, allowParallel = TRUE)
    xgbtreeModel = train(readmitted ~ raisedhands + Discussion + VisITedResources + AnnouncementsView + activity,
                         data = train, method = "xgbTree",tuneGrid = xgbTuneGrid,trainControl = ctrl)

    xgb_prediction <- predict(xgbtreeModel, test)
    xgb_probs <- predict(xgbtreeModel,test,type="prob")
    roc_xgboost =roc(test$readmitted, xgb_probs[,1])
    auc_xgboost = auc(roc_xgboost)
    #plot(roc_xgboost, col = flat_blue, main="ROC curve - xgboost" )
    confusionMatrix(xgb_prediction,test$readmitted)
    cor_xgboost = coords(roc_xgboost,"best", ret=c("accuracy", "sensitivity", "specificity"))