How Do You Interpret Area Under A Curve?

AUC represents the probability that a random positive (green) example is positioned to the right of a random negative (red) example. AUC ranges in value from 0 to 1. A model whose predictions are 100% wrong has an AUC of 0.0; one whose predictions are 100% correct has an AUC of 1.0.

What does area under curve measure?

A common use of the term “area under the curve” (AUC) is found in pharmacokinetic literature. It represents the area under the plasma concentration curve, also called the plasma concentration-time profile. … The AUC is a measure of total systemic exposure to the drug.

What does high area under the curve mean?

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.

What is a good area under the curve score?

Based on a rough classifying system, AUC can be interpreted as follows: 90 -100 = excellent; 80 – 90 = good; 70 – 80 = fair; 60 – 70 = poor; 50 – 60 = fail. In figure 1, the line (A) represents the ROC for an ideal diagnostic test.

What does AUC 0.75 mean?

An AUC of 0.75 would actually mean that lets say we take two data points belonging to separate classes then there is 75% chance model would be able to segregate them or rank order them correctly i.e positive point has a higher prediction probability than the negative class. (

What does the AUC tell you?

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.

What does area under the curve mean in pharmacokinetics?

In pharmacology, the area under the plot of plasma concentration of a drug versus time after dosage (called “area under the curve” or AUC) gives insight into the extent of exposure to a drug and its clearance rate from the body.

What does AUC less than 0.5 mean?

For assessing prediction performance, AUC of 0.5 is the worst result you can have. This means you have the predictive power of a coin flip, 50%/50% chance of being right or wrong. … AUC of <0.5 means that the absence of a positive result in your prediction predicts your true positive result.

How can I improve my AUC score?

One possible alternative (depending on your classification technique) is to use class weights instead using sampling techniques. Adding a greater penalty to misclassifying your under represented class can reduce bias without “over training” on the under-represented class samples.

Is AUC the same as accuracy?

For a given choice of threshold, you can compute accuracy, which is the proportion of true positives and negatives in the whole data set. AUC measures how true positive rate (recall) and false positive rate trade off, so in that sense it is already measuring something else.

What does AUC of 0.8 mean?

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.

How do you explain AUC from a probability perspective?

The AUC is the probability the model will score a randomly chosen positive class higher than a randomly chosen negative class.

What is the value of the area under the ROC curve AUC to conclude that a classifier is excellent?

What is the value of the area under the roc curve (AUC) to conclude that a classifier is excellent? The AUC value lies between 0.5 to 1 where 0.5 denotes a bad classifer and 1 denotes an excellent classifier.

What does AUC stand for in history?

AUC. abbreviation for (indicating years numbered from the founding of Rome, taken as 753 bc) ab urbe condita. anno urbis conditae.

What is the full form of AUC?