A neural network model is identifying a positive class point as a negative class sample and vice versa. What does it mean in terms of AUROC?Select an option Clear ResponseAUROC is approximately 100%.AUROC is approximately 80% with minimal overlapping between the classes.AUROC is approximately 50% with equal segregation between the classes.AUROC is approximately 0%.

What is the AUC?Review LaterThe area under the ROC curveThe area under the precision-recall curveThe area under the accuracy-complexity curveThe area under the learning curve

AUC-ROC score is more robust than Accuracy for imbalanced classes.

A model produces a perfect precision score of 1.0 for a positive class and sensitivity of 0.75. This means, the model may have mislabeled some positive instances as negative class. How can one judge the efficacy of the model in this scenario?Select an option Clear ResponseThrough F-measureThrough AUROCThrough accuracyThrough Specificity

You are evaluating a binary classifier. There are 50 positive outcomes in the test data, and 100 observations. Using a 50% threshold, the classifier predicts 40 positive outcomes, of which 10 are incorrect.Increasing the threshold to 60% results in 5 additional positive predictions, all of which are correct. Which of the following statements about this new model (compared with the original model that had a 50% threshold) is TRUE?0 / 1 pointThe F1 score of the classifier would decrease.The area under the ROC curve would decrease.The F1 score of the classifier would remain the same.The area under the ROC curve would remain the same.

In ROC analysis, a classifier is called ‘good’ if it has ______ Low TPR and Low FPR Low TPR and High FPR High TPR and Low FPR High TPR and High FPR