# Scoring for the comp
**URL:** https://community.drivendata.org/t/scoring-for-the-comp/6723
**Category:** Flu Shot Learning
**Created:** [November 5, 2021, 4:41am UTC](https://community.drivendata.org/t/scoring-for-the-comp/6723 "2021-11-05T04:41:11Z")
**Posts on this page:** 2
**Page:** 1
### Author:  [@dbissell](https://community.drivendata.org/u/dbissell)
#### Post date: [November 5, 2021, 4:41am UTC](https://community.drivendata.org/t/scoring-for-the-comp/6723/1 "2021-11-05T04:41:12Z")
I understand the scoring is calling for probabilities, not binary 0,1. How did the scorers/judges get these probabilities? It seems like judges only have access to the actual binary 0,1, unless they are mind readers. What am I not understanding?
---
### Author:  [@bogo1991](https://community.drivendata.org/u/bogo1991)
#### Post date: [December 14, 2021, 6:27am UTC](https://community.drivendata.org/t/scoring-for-the-comp/6723/2 "2021-12-14T06:27:47Z")
Hi, I am unable to understand your question. From my understanding, when you have a prediction of probabilities and you check the AUC under ROC against binary variables, you are going to get a number within 0 and 1. I work in R and I am checking my model’s efficiency in the fashion below.
```auto
#Probability Prediction
pred_seasonal <- predict(modFitgbm, val, type = 'prob')$X1
#Check Individual AUC
seasonal<-val$seasonal_vaccine
AUC(pred_seasonal,seasonal)
```
This gives me a value like 0.83234. That is the AUC my train and validation model is predicting. And I am getting decently close values with the test data so the test in not very far off.
Please let me know if this helps? Thanks!
A resource below which is compact and informative:
> **[Classification: ROC Curve and AUC | Machine Learning Crash Course...](https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc)**