7.00.02 - Receiver Operating Characteristic (ROC) - Aster Analytics

Teradata Aster® Analytics Foundation User GuideUpdate 2

Aster Analytics
Release Number
Release Date
September 2017
Content Type
Programming Reference
User Guide
Publication ID
English (United States)
The Receiver Operating Characteristic (ROC) function takes a set of prediction-actual pairs for a binary classification model and calculates the following values for a range of discrimination thresholds:
  • True positive rate (TPR)
  • False positive rate (FPR)
  • Area under the ROC curve (AUC)
  • Gini coefficient
A prediction-actual pair for a binary classifier consists of:
  • Predicted probability that an observation is in the positive class
  • Actual class of the observation

A discrimination threshold determines whether an observation is classified as positive (1) or negative (0). For example, suppose that a model predicts that an observation will be classified as positive with 0.55 probability. If the threshold above which an observation is classified as positive is 0.5, then the observation is classified as positive. If the threshold is 0.6, the observation is classified as negative.

You can generate prediction-actual pairs for ROC with these functions: