Demo: Predictive Modeling with BigML by David Gerster

1. 1
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
2

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
3

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!
6

7. Who Needs Labels?

8. Who Needs Labels?
8

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!
9

10. Thanks!
10
David Gerster
VP Data Science, BigML
gerster@bigml.com