@techreport{
 title = {ROC graphs: Notes and practical considerations for researchers},
 type = {techreport},
 year = {2005},
 source = {HP Laboratories},
 keywords = {classification,classifier evaluation,roc,visualization},
 websites = {http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:ROC+Graphs:+Notes+and+Practical+Considerations+for+Researchers#0},
 institution = {HP Laboratories},
 id = {57280120-8837-3e15-8ef7-5fffc32001c5},
 created = {2009-08-18T04:22:32.000Z},
 file_attached = {true},
 profile_id = {7edb4dd2-367e-3b20-8e26-a7e1d668fa33},
 last_modified = {2017-10-14T18:03:58.977Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Fawcett2005},
 source_type = {Technical report},
 private_publication = {false},
 abstract = {Receiver Operating Characteristics (ROC) graphs are a useful technique for organizing classifiers and visualizing their performance. ROC graphs are commonly used in medical decision making, and in recent years have been increasingly adopted in the machine learning and data mining research communities. Although ROC graphs are apparently simple, there are some common misconceptions and pitfalls when using them in practice. This article serves both as a tutorial introduction to ROC graphs and as a practical guide for using them in research.},
 bibtype = {techreport},
 author = {Fawcett, Tom}
}

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