Arun-George-Zachariah / AUC

Source code from the AUCCalculator jar (http://mark.goadrich.com/programs/AUC).

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:

• Java (version = 1.8)
• Python (version = 3.6)
• Ant
• Matplotlib

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

• Original AUCCalculator Jar
• Machine Learning Crash Course

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]