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

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%

Which of the following is the correct formula for Precision?Answer choicesSelect only one optionREVISITTrue Positive / (True Positive + False Positive)True Negative / (True Positive + False Positive)False Positive / (True Negative + Negative Positive)False Positive / (True Positive + False Positive)

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

Consider a classification problem with three classes: A, B, and C. A machine learning model is trained on a labeled dataset, and the confusion matrix for the model's predictions is given below:What is the overall accuracy of the model?a)0.69b)0.85c)0.8d)0.725

Given the confusion matrix for a disease prediction model, calculate the Precision, Recall, and F1-Score, and choose the correct values from the options below.1 pointPrecision: 0.800, Recall: 0.750, F1-Score: 0.774Precision: 0.778, Recall: 0.700, F1-Score: 0.737Precision: 0.850, Recall: 0.600, F1-Score: 0.706Precision: 0.900, Recall: 0.650, F1-Score: 0.756