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.What is the classifier’s Recall on the test sample?1 point25%60%75%80%

ou 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.What is the classifier’s Precision on the test sample?1 point25%60%75%80%

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.The threshold is now increased further, to 70%. Which of the following statements is TRUE?1 pointThe Recall of the classifier would Increase.The Precision of the classifier would decrease.The Recall of the classifier would increase or remain the same.The Precision of the classifier would increase or remain the same.

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.

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

write me a problem statement with answer on evaluation recall rate