Skip to content

Latest commit

 

History

History
37 lines (28 loc) · 1.57 KB

README.md

File metadata and controls

37 lines (28 loc) · 1.57 KB
Raw

AUC

Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) is one of the most important metric for evaluating a classification model, providing an aggregate measure of performance across all possible classification threshold.

Environment Setup

Requirements:

Build

ant build

Usage

java -jar auc.jar <FILE_NAME> <FILE_TYPE> <POS_COUNT> <NEG_COUNT> <MIN_RECALL>

  • FILE_NAME: Input data.
  • FILE_TYPE:
    • roc: false_positive_rate true_positive_rate
    • pr: recall precision
  • POS_COUNT: Count of the positive examples.
  • NEG_COUNT: Count of the negative examples.
  • MIN_RECALL: Minimum value to start calculating the area under the PR curve.

When FILE_TYPE is list i.e prob(example == true) true_classification(1 == positive, 0 = negative)
java -jar auc.jar <FILE_NAME> list <MIN_RECALL>

Plot ROC and PR Curves

cd scripts && python plot.py <FILE_NAME>

References

To Cite

Davis, Jesse, and Mark Goadrich. "The relationship between Precision-Recall and ROC curves." In Proceedings of the 23rd international conference on Machine learning, pp. 233-240. 2006. [PDF]