Sensitivity in ROC analysis is defined as:(Here, TP : True Positive, TN: True Negative, FP: False Positive, FN: False Negative) FP/ (FP + TN) FN / (TP + FN) TN/ (TN + FP) TP / (TP + FN)

Which of the following statements is correct: Sensitivity in ROC analysis is called True Positive Rate(tpr) Specificity in ROC analysis is not called True Negative Rate (tnr)  Specificity in ROC analysis is called True Positive Rate(tpr)  Sensitivity in ROC analysis is called True Negative Rate (tnr)

What does the ROC curve help determine in model evaluation?1 pointThe relative misclassification cost of the modelThe true-positive rate and false-positive rate for different criteria The optimal model based on diagnostic measuresThe model's statistical significanc

In ROC analysis, a classifier is called ‘good’ if it has ______ Low TPR and Low FPR Low TPR and High FPR High TPR and Low FPR High TPR and High FPR

AUC-ROC score is more robust than Accuracy for imbalanced classes.

Which one of the following statements about the receiver operating characteristic (ROC) is FALSE?Group of answer choicesThe TPR is a non-decreasing function of the FPR, i.e. the ROC has a non-negative gradient everywhere. To obtain a smooth ROC, we typically need to average the ROC obtained in multiple rounds of cross-validation.The AUC (Area Under the Curve) is never equal to one under any circumstances.To plot the ROC in a (0,1) classification problem, we can use the "roc_curve" function as follows:from sklearn import metricsimport matplotlib.pyplot as pltfpr, tpr, threshold = metrics.roc_curve(y,scores)plt.plot(fpr,tpr)When FPR = 0, TPR must be 0. When FPR = 1, TPR must be 1.