Exam DP-100 topic 1 question 22 discussion

you cannot visualize precision with ROC. True Positive Rate(on ROC's y axis) = Recall. Not precision. PR curve is used to visualize precision. I think I would go with Confusion matrix even though it requires further computations

it's important to note that while ROC is immensely useful for evaluating and comparing models, it doesn't directly display precision.

D. You should consider using the Binary classification confusion matrix visualization.

Based on https://learn.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml?view=azureml-api-2#binary-vs-multiclass-classification-metrics . Visualization for Classification: Not has Venn diagram; box plot diagram. Consider ROC (Receiver Operating Characteristic) and Binary classification confusion matrix. 'The receiver operating characteristic (ROC) curve plots the relationship between true positive rate (TPR) and false positive rate (FPR) as the decision threshold changes.' https://learn.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml?view=azureml-api-2#confusion-matrix Confusion matrix has 'precision to be used as the measurement', but Receiver operating characteristic has not.

This is the answer.

classification confusion matrix is the best accuracy measure

D for precision

Correct. https://builtin.com/data-science/precision-and-recall

precision, recall, f1-score. Are part of the confusion matrix, I agree that this answer is correct.

Shouldnt the answer be "B". ROC is a graph against TPR and FPR.. precision could be clearly visualized.