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

48.Consider a model that predicts 150 examples for the positive class, 95 are correct (true positives), meaning five were missed (false negatives) and 55 are incorrect (false positives). What will be the precision?  A. 55/150  B. 95/150  C. 55/95  D. 150/55

In a medical diagnosis task, where identifying false negatives is critical, which evaluation metric would be the primary focus?Review LaterSpecificitySensitivityPrecisionF1-Score

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 need to evaluate a classification model. Which metric can you use? ​​Mean squared error (MSE)PrecisionSilhouette

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.