This document discusses using anomaly detection for predictive modeling when labels are unavailable. It describes training an anomaly detector on biopsies without labels to generate anomaly scores, then using those scores as new labels to train a predictive model. The document explains that anomaly detection is useful for large unlabeled datasets to identify adversarial minority classes without needing to predefine what is being searched for.

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David Gerster
VP Data Science
gerster@bigml.com

2. Demo: Predictive Modeling
• Train a predictive model using 699 biopsies
• The “label” of benign or malignant is known for each one
• Since we have labels, this is supervised learning
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3. What if we don’t have labels?
• Can we get insight into our data if we don’t know the labels?
• Enter anomaly detection
• Since we don’t have labels, this is unsupervised learning
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4. 10 lines are needed
to isolate this data point
(not anomalous)

5. Only 4 lines are needed
to isolate this data point
(highly anomalous)

6. Demo: Anomaly Detection
• Remove the labels of benign or malignant
• Train an anomaly detector on this unlabeled data
• Create a new dataset with the anomaly scores as “labels”
• Use these “labels” to train a predictive model!
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7. Who Needs Labels?

8. Who Needs Labels?
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9. Minority Report
• Anomaly detection works great on large unlabeled datasets,
especially if you expect to find an (adversarial) minority class
• Millions of credit card transactions, billions of network events …
• Doesn’t require you to know what you’re looking for!
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10. Thanks!
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David Gerster
VP Data Science, BigML
gerster@bigml.com