Once your prediction has completed its analysis, you'll want to check the accuracy of the results. To assess the prediction accuracy, see the "AUC Score" at the top of the prediction of interest.
AUC stands for "Area Under the Curve" and refers to the ROC Curve used in Machine Learning algorithms to weigh the True Positive vs. False Positive results of a predicted user set.
Basically if a prediction has an AUC Score > 65%, the model has decent predictive power in classifying users likely to perform the goal correctly.
If the AUC Score < 50%, then you'd have better odds flipping a coin, and it is not recommended that you use the resulting prediction.