User-Operated Model-Building Systems - Data Science: Inconvenient Truths
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Transcript of User-Operated Model-Building Systems - Data Science: Inconvenient Truths
PROOF OF FAILURE
Clare CorthellMachine Learning Engineer & Data Scientist
@clarecorthellwww.datasciencemasters.org
Machine Learning Need
• Very little structured information
• Disaggregated data
• Need for categorization
=> Data Structuring & Creation
REINFORCEMENT PITFALLS
- (Technical) Overfitting- Humans have to question their own assumptions- Dimensional encoding issues (is this expressible in features?)- Human definitions is inadequate
WHY IS SVM BETTER?Feature inspectability
• sometimes for debugging• mostly for humans
Humans don’t know what transformation the black box exerts on inputs. But sometimes, they need to know.
Their investors, their customers, their data analysts, their operators, their CEO — all want to know.
HUMANS SHOULD BE HUMANSCOMPUTERS SHOULD BE COMPUTERS.
Sometimes, our identities get a little mixed up.
1. Set Expectationsmake sure the organization understands failures
2. Reduce the “Trickery”*We build systems for humans. They need to understand how the levers and knobs affect the outcome
*h/t Sean Taylor
datasciencemasters.org
@clarecorthell
mattermark.com