COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
|
Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
|
|
|
Automatic under curve
|
|
|
Area Under Curve
|
|
|
Addition under curve
|
|
|
Association under curve
|
Detailed explanation-1: -AUC stands for “Area under the ROC Curve.” That is, AUC measures the entire two-dimensional area underneath the entire ROC curve (think integral calculus) from (0, 0) to (1, 1). Figure 5. AUC (Area under the ROC Curve). AUC provides an aggregate measure of performance across all possible classification thresholds.
Detailed explanation-2: -When we need to check or visualize the performance of the multi-class classification problem, we use the AUC (Area Under The Curve) ROC (Receiver Operating Characteristics) curve. It is one of the most important evaluation metrics for checking any classification model’s performance.
Detailed explanation-3: -AUC :Area under curve (AUC) is also known as c-statistics. Some statisticians also call it AUROC which stands for area under the receiver operating characteristics. It is calculated by adding Concordance Percent and 0.5 times of Tied Percent.
Detailed explanation-4: -The area under the plasma drug concentration-time curve (AUC) reflects the actual body exposure to drug after administration of a dose of the drug and is expressed in mg*h/L.
Detailed explanation-5: -The area under the ROC curve (AUC) results were considered excellent for AUC values between 0.9-1, good for AUC values between 0.8-0.9, fair for AUC values between 0.7-0.8, poor for AUC values between 0.6-0.7 and failed for AUC values between 0.5-0.6.