What Does the Auc Tell Us
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...
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 considered a good AUC?
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.
How do you explain AUC from a probability perspective?
The AUC is the area under the ROC curve. It is a number between zero and one, because the ROC curve fits inside a unit square. Any model worth much of anything has an AUC larger than 0.5, as the line segment running between (0, 0) and (1, 1) represents a model that randomly guesses class membership.
Alexander Ross has covered the video game industry for a decade, writing deep dives on game design, esports tournaments, VR developments, and gaming culture.
