AuC-ROC

Area Under the Receiver Operating Characteristic Curve

A performance metric used in machine learning (ML), particularly in classification problems. It’s part of the ROC (Receiver Operating Characteristic) curve analysis. The ROC curve is a graphical representation of the performance of a classification model at various threshold settings.

The AuC-ROC measures the entire two-dimensional area underneath the entire ROC curve from (0,0) to (1,1). This score provides an aggregate measure of performance across all possible classification thresholds. A model with a higher AuC score generally indicates better performance, as it suggests that the model can effectively distinguish between the positive and negative classes.

Understanding and utilizing AuC scores can be crucial when developing predictive models for customer behavior, segmentation, or targeting. High AuC scores in models predicting customer responses or preferences can lead to more effective marketing strategies and improved sales outcomes.

Also commonly referred to as AuC.

• Abbreviation: AuC-ROC
• Source: What is Artificial Intelligence