WK 4: readings

You will find a number of papers on ROC curves in the Week 4 Reading Folder. I suggest reading the papers to try to digest how ROC curves are commonly used in Machine Learning after our lecture on Tuesday to solidify your understanding of ROC curve analysis.

Required Reading:

ROC Graphs: Notes and Practical Considerations for Data Mining Researchers

The Relationship Between Default Prediction And Lending Profits:Integrating ROCAnalysis And Loan Pricing (Try your best to get the point of how Costs may be combined with ROC curve analysis)

Crafting Papers on Machine Learning (This is a good note to read as you are thinking through your project ideas)

Reference Reading:

The Case Against Accuracy Estimation for Comparing Classifiers, Provost, F., T. Fawcett, and R. Kohavi, In Proceedings of the Fifteenth International Conference on Machine Learning (ICML-98).

On the Optimality of the Simple Bayesian Classifier under Zero-One Loss