Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1uftCWH. Daniel Tunkelang focuses on the data science mindset for successfully applying machine learning to solve problems: express, explain, experiment. Filmed at qconsf.com. Daniel Tunkelang leads LinkedIn's efforts around query understanding. Before that, he led LinkedIn's product data science team.

1. Recruiting Solutions
1
My Three Ex’s:
A Data Science Approach for
Applied Machine Learning

2. Watch the video with slide
synchronization on InfoQ.com!
http://www.infoq.com/presentations
/data-science-machine-learning
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3. Presented at QCon San Francisco
www.qconsf.com
Purpose of QCon
- to empower software development by facilitating the spread of
knowledge and innovation
Strategy
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Highlights
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4. Dedicated to 3 of my favorite ex co-workers.

5. First, a disclosure.
This isn’t a talk about machine learning.
It’s a talk about applying machine learning.
What’s the difference?
3

6. Let’s talk about something else for a moment.
Hash Tables
4

7. What you (need to) know about hash tables.
Theory Application
5
Class HashMap<K,V>
java.lang.Object
java.util.AbstractMap<K,V>
java.util.HashMap<K,V>
Type Parameters:
K - the type of keys maintained by this map
V - the type of mapped values
All Implemented Interfaces:
Serializable, Cloneable, Map<K,V>

8. Now let’s get back to machine learning!
6

9. Please allow me to introduce my three ex’s.
Express.
Explain.
Experiment.
7

10. Embrace the data science mindset.
Express
Understand your utility and inputs.
Explain
Understand your models and metrics.
Experiment
Optimize for the speed of learning.
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11. Express.
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12. How to train your machine learning model.
1. Define your objective function.
2. Collect training data.
3. Build models.
4. Profit!
10

13. You can only improve what you measure.
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Clicks?
Actions?
Outcomes?

14. Be careful how you define precision…
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15. Account for non-uniform inputs and costs.
13

16. Stratified sampling is your friend.
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17. An example of segmenting models.
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Searcher: Recruiter
Query: Person Name
Searcher: Job Seeker
Query: Person Name
Searcher: Recruiter
Query: Job Title
Searcher: Job Seeker
Query: Job Title

18. Express yourself in your feature vectors.
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19. Express: Summary.
 Choose an objective function that models utility.
 Be careful how you define precision.
 Account for non-uniform inputs and costs.
 Stratified sampling is your friend.
 Express yourself in your feature vectors.
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20. Explain.
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21. With apologies to the little prince.
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22. Everyone is talking about Deep Learning.
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23. But accuracy isn’t everything.
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24. Explainable models, explainable features.
 Less is more when it comes to explainability.
 Algorithms can protect you from overfitting, but they can’t
protect you from the biases you introduce.
 Introspection into your models and features makes it
easier for you and others to debug them.
 Especially if you don’t completely trust your objective
function or the representativeness of your training data.
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25. Linear regression? Decision trees?
 Linear regression and decision trees favor explainability
over accuracy, compared to more sophisticated models.
 But size matters. If you have too many features or too
deep a decision tree, you lose explainability.
 You can always upgrade to a more sophisticated model
when you trust your objective function and training data.
 Build a machine learning model is an iterative process.
Optimize for the speed of your own learning.
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26. Explain: Summary.
 Accuracy isn’t everything.
 Less is more when it comes to explainability.
 Don’t knock linear models and decision trees!
 Start with simple models, then upgrade.
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27. Experiment.
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28. Why experiments matter.
“You have to kiss a lot of frogs to find one prince.
So how can you find your prince faster?
By finding more frogs and kissing them faster and
faster.”
-- Mike Moran
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29. Life in the age of big data.
Yesterday Today
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Experiments are expensive,
choose hypotheses wisely.
Experiments are cheap,
do as many as you can!

30. So should we just test everything?
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31. Optimize for the speed of learning.
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vs

32. Be disciplined: test one variable at a time.
• Autocomplete
• Entity Tagging
• Vertical Intent
• # of Suggestions
• Suggestion Order
• Language
• Query Construction
• Ranking Model
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33. Experiment: Summary.
 Kiss lots of frogs: experiments are cheap.
 But test in good faith – don’t just flip coins.
 Optimize for the speed of learning.
 Be disciplined: test one variable at a time.
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34. Bringing it all together.
Express
Understand your utility and inputs.
Explain
Understand your models and metrics.
Experiment
Optimize for the speed of learning.
32

35. 33
Daniel Tunkelang
dtunkelang@linkedin.com
https://linkedin.com/in/dtunkelang

36. Watch the video with slide synchronization on
InfoQ.com!
http://www.infoq.com/presentations/data-science-
machine-learning