AI Classification Pitfalls and Cosine Similarity
Explore the challenges of AI classification with scenarios like predisposition testing and cosine similarity calculations. Learn how to avoid common pitfalls in machine learning models.
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Presentation Transcript
CYBR CSB AI Day The Myth of AI NLP Word2Vec demo ML Classification (e.g., spam?) Used Vocab quizzes CVE classification gms
Classifier Pitfall 1 in 1000 people have a certain predisposition to a deadly disease for which a daily pill will prevent it from activating. There is a test that is 95% accurate for diagnosing the predisposition. Imagine that you are tested and receive a positive result notification what is the likelihood that you actually have the predisposition? gms
1 in 1000 95% accurate TP/FP and TN/FN? 1 / 49 950 / 0 gms
Used Vocab quizzes MIS 213 (557 terms) and MIS 365 (442) Distractor selection MSCSIS Capstone ~3% of published CVEs are exploited in the wild Use CISA KEV Classifier based on select CVE fields gms
Cosine Similarity https://www.omnicalculator.com/math/cosine-similarity a = [-0.4, 0.8] and b = [-0.3, 0.2] a.b = (-0.4)(-0.3) + (0.8)(0.2) = 0.28 ||a|| = [(-0.4) + (0.8) ].5 = 0.8944 ||b|| = [(-0.3) + (0.2) ].5 = 0.3606 a.b = 0.28 / (0.8944 * 0.3606) a.b = .8682 gms
