Machine Learning in 5 Minutes— Classification
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Slides from a lightning talk on classification methods, originally given at Open Source Open Mic Chicago 01/2016. Yes, I know I left things you. You try covering this in 5 minutes.
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Machine Learning in 5 Minutes— Classification
- 1. classification edition Machine Learning in 5 Minutes Brian Lange
- 2. hi, i’m a data scientist
- 4. popular examples -spam filters -the Sorting Hat
- 5. things to know - you need data labeled with the correct answers to “train” these algorithms before they work - feature = dimension = attribute of the data - class = category = Harry Potter house
- 6. linear discriminants “draw a line through it”
- 7. linear discriminants “draw a line through it”
- 8. linear discriminants “draw a line through it”
- 9. linear discriminants “draw a line through it” 🎉
- 10. define what “shitty” means 6 wrong
- 11. define what “shitty” means 4 wrong
- 12. a map of shittiness to find the least shitty line shittiness slope intercept
- 13. probably don’t use these linear discriminants:
- 14. logistic regression “divide it with a log function”
- 15. logistic regression “divide it with a log function” 🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉 + gives you probabilities + the model is a formula + can “threshold” to make model more or less conservative 💩💩💩💩💩💩💩💩💩💩💩 - only works with linear decision boundaries
- 16. SVMs (support vector machines) “*advanced* draw a line through it” - better definition of “shitty” - lines can turn into non-linear shapes if you transform your data
- 19. 💩
- 21. 💩
- 25. SVMs (support vector machines) “*advanced* draw a line through it”
- 26. SVMs (support vector machines) “*advanced* draw a line through it” 🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉 works well on a lot of different shapes of data thanks to the kernel trick 💩💩💩💩💩💩💩💩💩💩💩 not super easy to explain to people can only kinda do probabilities
- 27. KNN (k-nearest neighbors) “what do similar cases look like?”
- 28. KNN (k-nearest neighbors) “what do similar cases look like?” k=1
- 29. KNN (k-nearest neighbors) “what do similar cases look like?” k=2
- 30. KNN (k-nearest neighbors) “what do similar cases look like?” k=1
- 31. KNN (k-nearest neighbors) “what do similar cases look like?” k=2
- 32. KNN (k-nearest neighbors) “what do similar cases look like?” k=3
- 33. KNN (k-nearest neighbors) “what do similar cases look like?”
- 34. KNN (k-nearest neighbors) “what do similar cases look like?” 🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉 + no training, adding new data is easy + you get to define “distance” 💩💩💩💩💩💩💩💩💩💩💩 - can be outlier-sensitive - you have to define “distance”
- 35. decision tree learners make a flow chart of it
- 36. decision tree learners make a flow chart of it x < 3? yes no 3
- 37. decision tree learners make a flow chart of it x < 3? yes no y < 4? yes no 3 4
- 38. decision tree learners make a flow chart of it x < 3? yes no y < 4? yes no x < 5? yes no 3 5 4
- 39. decision tree learners make a flow chart of it 🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉 + fit all kinds of arbitrary shapes + output is a clear set of conditionals 💩💩💩💩💩💩💩💩💩💩💩 - extremely prone to overfitting - have to rebuild when you get new data - no probability estimates
- 40. ensemble models make a bunch of models and combine them
- 41. ensemble models make a bunch of models and combine them 🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉 - don’t overfit as much as their component parts - Generally don’t require much parameter tweaking - If data doesn’t change very often, you can make them semi-online by just adding new trees - Can provide probabilities 💩💩💩💩💩💩💩💩💩💩💩 - Slower than their component parts (though if those are fast, it doesn’t matter)