Adding Tree and Tree
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Brushfire: Distributed Decision Tree Ensemble Learning in Scala
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Adding Tree and Tree
- 1. Adding tree and tree @avibryant
- 2. Brushfire:! Distributed, Generic, Decision Tree Learning in Scala (using Hadoop) @avibryant Open source: Real Soon Now
- 3. Vun!
- 4. Two! +
- 5. Tree!
- 7. Do you like cookies? {height: 5, color: blue, wears: fur} ? {height: 7, color: yellow, wears: feathers} ? {height: 3, color: green, wears: garbage} ? {height: 5, color: yellow, wears: stripes} ? {height: 4, color: orange, wears: stripes} ?
- 8. Do you like cookies? color != blue color = blue
- 9. Does Cookie Monster like Cookies? color != blue color = blue
- 10. Is Cookie Monster Blue? color != blue color = blue
- 11. Cooooookie! color != blue color = blue cookie!
- 12. Do you like cookies? color != blue color = blue yuck ok cookie! wears != stripes wears = stripes
- 13. color != blue color = blue T T T wears != stripes wears = stripes
- 14. color != blue color = blue T T T wears != stripes wears = stripes Do you like cookies? How many cookies will you eat? What’s your favorite kind of cookie?
- 15. Bootstrap or k-fold? Chi-square or entropy? Wow! Classification or regression? Binary splits or multiway? Out-of-bag or out-of-time? One tree or many? Binary or multi-class?
- 16. trait Evaluator[V,T] trait Tree[V,T] trait Splitter[V,T] trait Error[T,E] Wow! Such types! case class Instance[V,T]
- 17. false true false true Binary classification
- 18. 0.1 0.4 0.0 0.9 Binary classification
- 19. T+T+T+T= T T T T T+T+T+T+T= T+T+T+T+T= T+T+T=
- 22. Bigger (data) = Better (models) Generic != Fast “Why do you rob banks?”
- 23. Learning a tree in Scalding 11 passes through the data 21 MapReduce steps
- 24. T T
- 25. T T T T
- 26. T T T T T T T T
- 27. Step 1/21 T
- 28. {height: 5, color: blue, wears: fur} {height: 7, color: yellow, wears: feathers} {height: 3, color: green, wears: garbage} {height: 5, color: yellow, wears: stripes} {height: 4, color: orange, wears: stripes}
- 29. Instance[V,T] Instance[V,T] Instance[V,T] Instance[V,T] Instance[V,T] Instance[V,T] Instance[V,T] T T T T T T T T T T Map T Reduce
- 30. color != blue = blue T T color != yellow = yellow T T height < 5 >= 5 T T ? Step 2/21
- 31. color != blue = blue T T color Step 2/21 != yellow = yellow T T ?
- 32. blue yellow green yellow orange
- 33. blue yellow green yellow orange
- 34. Instance[V,T] Instance[V,T] Instance[V,T] Instance[V,T] Instance[V,T] Instance[V,T] Instance[V,T] S S S S S S S S Map Reduce Step 2/21 S S Other options: CountMinSketch QTree …
- 35. V => Boolean V => Boolean T T
- 36. V => Boolean V => Boolean T T T V => Boolean
- 38. Instance[V,T] Instance[V,T] Instance[V,T] Instance[V,T] Instance[V,T] Instance[V,T] Instance[V,T] S S S S S S S S Step 3/21 S S S Split[V,T] Split[V,T] Split[V,T] Split[V,T]
- 40. Instance[V,T] Instance[V,T] Instance[V,T] Instance[V,T] Instance[V,T] Instance[V,T] Instance[V,T] S S S S S S S S S Step 3/21 S S S Split[V,T] Split[V,T] Split[V,T] Split[V,T] S S S S S S
- 41. Instance[V,T] Instance[V,T] Instance[V,T] Instance[V,T] Instance[V,T] Instance[V,T] Instance[V,T] S S S S S S S S S Step 3/21 S S S Split[V,T] Split[V,T] Split[V,T] Split[V,T] S S S S S S S S S S Split[V,T] Split[V,T] Split[V,T]
- 43. Instance[V,T] Instance[V,T] Instance[V,T] … Forests!
- 48. V? {height: 5, color: blue, wears: fur} ? {height: 7, color: yellow, wears: feathers} ? {height: 3, color: green, wears: garbage} ? {height: 5, color: yellow, wears: stripes} ? {height: 4, color: orange, wears: stripes} ?
- 50. PLANET http://static.googleusercontent.com/media/ research.google.com/en/us/pubs/archive/36296.pdf Scalding + Algebird http://github.com/twitter/scalding http://github.com/twitter/algebird Coming soon http://github.com/stripe/brushfire