Feature Engineering The Dark Art of Data Science Josh Wills // Senior Director of Data Science
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Feature Engineering The Dark Art of Data Science
Josh Wills // Senior Director of Data Science
2 © 2014 Cloudera, Inc. All rights reserved.
About Me
3 © 2014 Cloudera, Inc. All rights reserved.
The Two Kinds of Data Scientists
• The Lab • Statisticians who got really
good at programming • Neuroscientists,
geneticists, social scientists, etc.
• The Factory • Software engineers who
were in the wrong place at the wrong time
4 © 2014 Cloudera, Inc. All rights reserved.
A Brief History of Data Products
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Scorecards
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Spell Correction
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Virtual Personal Assistants
• Pipeline of not-so-loosely coupled ML systems
• Speech recognition • Semantic decoding • Intent Model • Dialogue Rules • Language Generation • Speech Synthesis
8 © 2014 Cloudera, Inc. All rights reserved.
Machine Learning vs. Feature Extraction
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Talking About Feature Engineering
10 © 2014 Cloudera, Inc. All rights reserved.
Brainwash
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Good: More Features == Better Performance
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Good: Feature Development Scales
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Bad: Lots of Grunt Work
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Deployment: Even More Grunt Work
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Analytic Data Model: Giant Spreadsheet
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Operational Data Model: 3NF
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The Impedance Mismatch
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What Do We Need?
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One Solution I Thought Might Work
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Inventing on Principle
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A Data Model For Feature Engineering
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Bridging the Gap
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Spell Correction Revisited
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A Simple Star Schema for Search
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A Supernova Schema for Search
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Beyond Analytic SQL: Nested SQL Sessions
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Exhibit (http://github.com/jwills/exhibit)
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Operational Supernovas
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ln(supernova)
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Data Science and the Holy Grail
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Feature Engineering: Within / Across
1. Generate features for normalization/segmentation.
2. Generate normalization constants and segments using the result of Step 1.
3. Generate input features using the original data and the result of Step 2.
4. Generate a model using the result of Step 3.
32 © 2014 Cloudera and/or its affiliates. All rights reserved.
Feature Engineering IDE
33 © 2014 Cloudera, Inc. All rights reserved.
Data Science as ETL Workflow
34 © 2014 Cloudera, Inc. All rights reserved.
Metlife’s Wall • A 360 degree view of all customer
data and interactions
• Backed by a NoSQL, supernova-style data model (MongoDB, in this instance)
• Multiple Use Cases • Customer Support • Exploratory Analytics/Metrics Definitions • Profiles/Segmentation
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Queryable Wall
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Integrated Model and Feature Store
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Feature Application and Evaluation
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Exhibitor (http://github.com/jhlch/exhibitor)
Thank you.
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