Which evaluation metric is commonly used for binary classification problems and measures the proportion of true positive predictions among all positive examples?Select one:a. Recallb. Precision

Which evaluation metric measures the percentage of recommended items that are relevant to the user?Question 10Answera.Precisionb.F1-scorec.Mean Average Precision (MAP)d.Recall

In a classification problem, what metric would you use to measure the performance of a model when the classes are imbalanced?*1 pointo A) Accuracyo B) Precisiono C) Recallo D) F1-score

Question 3Which tool is most appropriate for measuring the performance of a classifier on unbalanced classes? 1 pointThe precision-recall curve.The true positive rate.The Receiver Operating Characteristic (ROC) curve. The false positive rate.

In the context of multiclass classification, which evaluation metric takes into account both the false positive and false negative errors for each class, providing a more comprehensive view of model performance?Review LaterMacro F1-ScoreAccuracyHamming LossMicro F1-Score

24. A data scientist has trained a binary classification model to detect whether an email is spam or not. He now wants to evaluate the perfomance of the model on a test dataset. The test dataset contains 100 samples. 80 of the samples in the test dataset are records of emails which are not spam. The model correctly predicted 70 emails as not spam. It also correctly predicted 12 emails as spam. Which of the following statements about the metrics of the model is true? - Recall for spam class is 0.6 and recall for not spam class is 0.875- Accuracy for the model is 81 percent- Precision for spam class in 0.6 and recall for not spam class in 0.875- Precision for the spam class is 0.6 and precision for the not spam class is 0.875- Recall for the spam class is 0.545 and recall for the not spam class is 0.8971 of the 5 listed2 of the 5 listed3 of the 5 listed4 of the 5 listedNone of the listed