Area Under the Curve (AUC) This is **the probability that an observation with a positive class will have a greater predicted probability than an observation in a negative class**. If AUC = 1, it means there is perfect prediction by the model.

## What is AUC in Analytics?

The Area Under the Curve (AUC) is the measure of the ability of a classifier to distinguish between classes and is used as a summary of the ROC curve. The higher the AUC, the better the performance of the model at distinguishing between the positive and negative classes.

## How do you interpret area under a curve?

AREA UNDER THE ROC CURVE In general, an AUC of 0.5 suggests no discrimination (i.e., ability to diagnose patients with and without the disease or condition based on the test), 0.7 to 0.8 is considered acceptable, 0.8 to 0.9 is considered excellent, and more than 0.9 is considered outstanding.

## What does area under the curve mean in AI?

Area Under the Curve (AUC) is a common term in outsourcing businesses in the field of Artificial Intelligence. It is a methodology used in Machine Learning to evaluate several used models to determine which one has the highest level of performance.

## What does area under the ROC curve mean?

As the area under an ROC curve is a measure of the usefulness of a test in general, where a greater area means a more useful test, the areas under ROC curves are used to compare the usefulness of tests. The term ROC stands for Receiver Operating Characteristic.

## What does area under the curve mean in pharmacokinetics?

From Wikipedia, the free encyclopedia. In the field of pharmacokinetics, the area under the curve (AUC) is the definite integral of a curve that describes the variation of a drug concentration in blood plasma as a function of time (this can be done using liquid chromatography–mass spectrometry).

## Is AUC or accuracy better?

AUC is in fact often preferred over accuracy for binary classification for a number of different reasons.

## What’s a good AUC score?

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.

## What is the AUC score?

AUC score measures the total area underneath the ROC curve. AUC is scale invariant and also threshold invariant. In probability terms, AUC score is equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one.

## What does area under the curve mean in statistics?

ROC plot. Difference between the areas under two curves. Related tasks. Plotting a single ROC curve. Comparing two or more ROC curves.

## What does area under the curve mean in physics?

The area under the curve is the magnitude of the displacement, which is equal to the distance traveled (only for constant acceleration).

## How is area under ROC calculated?

If the ROC curve were a perfect step function, we could find the area under it by adding a set of vertical bars with widths equal to the spaces between points on the FPR axis, and heights equal to the step height on the TPR axis.

## What is area under the curve used for?

The AUC is a measure of total systemic exposure to the drug. AUC is one of several important pharmacokinetic terms that are used to describe and quantify aspects of the plasma concentration-time profile of an administered drug (and/or its metabolites, which may or may not be pharmacologically active themselves).

## How can I improve my AUC score?

In order to improve AUC, it is overall to improve the performance of the classifier. Several measures could be taken for experimentation. However, it will depend on the problem and the data to decide which measure will work.