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classification edition
Machine Learning in 5 Minutes
Brian Lange
hi, i’m a
data scientist
classification
algorithms
popular examples
-spam filters
-the Sorting Hat
things to know
- you need data labeled with the correct answers to
“train” these algorithms before they work
- feature = d...
linear discriminants
“draw a line through it”
linear discriminants
“draw a line through it”
linear discriminants
“draw a line through it”
linear discriminants
“draw a line through it”
🎉
define what “shitty” means
6 wrong
define what “shitty” means
4 wrong
a map of shittiness
to find the least shitty line
shittiness
slope
intercept
probably don’t use these
linear discriminants:
logistic regression
“divide it with a log function”
logistic regression
“divide it with a log function”
🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉
+ gives you probabilities
+ the model is a formula
+ can “...
SVMs (support vector machines)
“*advanced* draw a line through it”
- better definition of “shitty”
- lines can turn into no...
💩
💩
“the kernel trick”
🎉
woooooooooooo
🎉🎉
SVMs (support vector machines)
“*advanced* draw a line through it”
SVMs (support vector machines)
“*advanced* draw a line through it”
🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉
works well on a lot of different shapes of d...
KNN (k-nearest neighbors)
“what do similar cases look like?”
KNN (k-nearest neighbors)
“what do similar cases look like?”
k=1
KNN (k-nearest neighbors)
“what do similar cases look like?”
k=2
KNN (k-nearest neighbors)
“what do similar cases look like?”
k=1
KNN (k-nearest neighbors)
“what do similar cases look like?”
k=2
KNN (k-nearest neighbors)
“what do similar cases look like?”
k=3
KNN (k-nearest neighbors)
“what do similar cases look like?”
KNN (k-nearest neighbors)
“what do similar cases look like?”
🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉
+ no training, adding new data is easy
+ you get ...
decision tree learners
make a flow chart of it
decision tree learners
make a flow chart of it
x < 3?
yes no
3
decision tree learners
make a flow chart of it
x < 3?
yes no
y < 4?
yes no
3
4
decision tree learners
make a flow chart of it
x < 3?
yes no
y < 4?
yes no
x < 5?
yes no
3 5
4
decision tree learners
make a flow chart of it
🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉
+ fit all kinds of arbitrary shapes
+ output is a clear set of
co...
ensemble models
make a bunch of models and combine them
ensemble models
make a bunch of models and combine them
🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉
- don’t overfit as much as their component parts
- Gene...
Machine Learning in 5 Minutes— Classification
Machine Learning in 5 Minutes— Classification
Machine Learning in 5 Minutes— Classification
Machine Learning in 5 Minutes— Classification
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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.

Veröffentlicht in: Technologie

Machine Learning in 5 Minutes— Classification

  1. 1. classification edition Machine Learning in 5 Minutes Brian Lange
  2. 2. hi, i’m a data scientist
  3. 3. classification algorithms
  4. 4. popular examples -spam filters -the Sorting Hat
  5. 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. 6. linear discriminants “draw a line through it”
  7. 7. linear discriminants “draw a line through it”
  8. 8. linear discriminants “draw a line through it”
  9. 9. linear discriminants “draw a line through it” 🎉
  10. 10. define what “shitty” means 6 wrong
  11. 11. define what “shitty” means 4 wrong
  12. 12. a map of shittiness to find the least shitty line shittiness slope intercept
  13. 13. probably don’t use these linear discriminants:
  14. 14. logistic regression “divide it with a log function”
  15. 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. 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
  17. 17. 💩
  18. 18. 💩
  19. 19. “the kernel trick”
  20. 20. 🎉 woooooooooooo 🎉🎉
  21. 21. SVMs (support vector machines) “*advanced* draw a line through it”
  22. 22. 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
  23. 23. KNN (k-nearest neighbors) “what do similar cases look like?”
  24. 24. KNN (k-nearest neighbors) “what do similar cases look like?” k=1
  25. 25. KNN (k-nearest neighbors) “what do similar cases look like?” k=2
  26. 26. KNN (k-nearest neighbors) “what do similar cases look like?” k=1
  27. 27. KNN (k-nearest neighbors) “what do similar cases look like?” k=2
  28. 28. KNN (k-nearest neighbors) “what do similar cases look like?” k=3
  29. 29. KNN (k-nearest neighbors) “what do similar cases look like?”
  30. 30. 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”
  31. 31. decision tree learners make a flow chart of it
  32. 32. decision tree learners make a flow chart of it x < 3? yes no 3
  33. 33. decision tree learners make a flow chart of it x < 3? yes no y < 4? yes no 3 4
  34. 34. decision tree learners make a flow chart of it x < 3? yes no y < 4? yes no x < 5? yes no 3 5 4
  35. 35. 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
  36. 36. ensemble models make a bunch of models and combine them
  37. 37. 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)

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