1. A [somewhat] Quick Overview of Probability Shannon Quinn CSCI 6900 (with thanks to William Cohen of Carnegie Mellon)

2. Tuesday’s example Starting point: Google books 5-gram data – All 5-grams that appear >= 40 times in a corpus of 1M English books approx 80B words 5-grams: 30Gb compressed, 250-300Gb uncompressed Each 5-gram contains frequency distribution over years – Wrote code to compute Pr(A,B,C,D,E|C=affect or C=effect) Pr(any subset of A,…,E|any other fixed values of A,…,E with C=affect V effect )

3. The Joint Distribution Recipe for making a joint distribution of M variables: 1.Make a truth table listing all combinations of values of your variables (if there are M Boolean variables then the table will have 2 M rows). 2.For each combination of values, say how probable it is. 3.If you subscribe to the axioms of probability, those numbers must sum to 1. Example: Boolean variables A, B, C ABCProb 0000.30 0010.05 0100.10 0110.05 100 1010.10 1100.25 1110.10 A B C 0.05 0.25 0.100.05 0.10 0.30

4. Some of the Joint Distribution ABCDEp istheeffectofthe0.00036 istheeffectofa0.00034.Theeffectofthis0.00034 tothiseffect:“0.00034 betheeffectofthe… …………… nottheeffectofany0.00024 …………… doesnotaffectthegeneral0.00020 doesnotaffectthequestion0.00020 anymanneraffecttheprinciple0.00018

5. An experiment: how useful is the brute-force joint classifier? Extracted all affect/effect 5-grams from an old Reuters corpus – about 20k documents – about 723 n-grams, 661 distinct – Financial news, not novels or textbooks Tried to predict center word with: – Pr(C|A=a,B=b,D=d,E=e) – then P(C|A,B,D) – then P(C|B,D) – then P(C|B) – then P(C)

6. EXAMPLES – “The cumulative _ of the”  effect (1.0) – “Go into _ on January”  effect (1.0) – “From cumulative _ of accounting” not present in train data Nor is ““From cumulative _ of _” But “_ cumulative _ of _”  effect (1.0) – “Would not _ Finance Minister” not present But “_ not _ _ _”  affect (0.9625)

7. What Have We Learned? Counting’s not enough -? Counting goes a long way with big data -? Big data can sometimes be made small – For a specific task, like this one – It’s all in the data preparation -? Often density estimation is more important than classification

8. Copyright © Andrew W. Moore Density Estimation Our Joint Distribution learner is our first example of something called Density Estimation A Density Estimator learns a mapping from a set of attributes values to a Probability Density Estimator Probability Input Attributes

9. Copyright © Andrew W. Moore Density Estimation Compare it against the two other major kinds of models: Regressor Prediction of real-valued output Input Attributes Density Estimator Probability Input Attributes Classifier Prediction of categorical output or class Input Attributes One of a few discrete values

10. Copyright © Andrew W. Moore Density Estimation  Classification Density Estimator P( x,y) Input Attributes Classifier Prediction of categorical output Input Attributes x One of y1, …., yk Class To classify x 1.Use your estimator to compute P( x, y1), …., P( x, yk) 2.Return the class y* with the highest predicted probability Ideally is correct with P(x,y*) = P(x,y *)/ (P( x,y1) + …. + P( x,yk)) ^ ^ ^^ ^^ ^ Binary case: predict POS if P( x )>0.5 ^

11. Classification vs Density Estimation ClassificationDensity Estimation

12. Probability - what you need to really, really know Probabilities are cool Random variables and events The Axioms of Probability Independence, binomials, multinomials Conditional probabilities Bayes Rule MLE’s, smoothing, and MAPs The joint distribution Inference Density estimation and classification

13. Probability - what you need to really, really know Probabilities are cool Random variables and events The Axioms of Probability Independence, binomials, multinomials Conditional probabilities Bayes Rule MLE’s, smoothing, and MAPs The joint distribution Inference Density estimation and classification

14. Independent Events Definition: two events A and B are independent if Pr(A and B)=Pr(A)*Pr(B). Intuition: outcome of A has no effect on the outcome of B (and vice versa). – We need to assume the different rolls are independent to solve the problem. – You frequently need to assume the independence of something to solve any learning problem.

15. Practical independence You’re the DM in a D&D game. Joe brings his own d20 and throws 4 critical hits in a row to start off – DM=dungeon master – D20 = 20-sided die – “Critical hit” = 19 or 20 What are the odds of that happening with a fair die? Ci=critical hit on trial i, i=1,2,3,4 P(C1 and C2 … and C4) = P(C1)*…*P(C4) = (1/10)^4

16. Multivalued Discrete Random Variables Suppose A can take on more than 2 values A is a random variable with arity k if it can take on exactly one value out of {v 1,v 2,.. v k } – Example: V={aaliyah, aardvark, …., zymurge, zynga} – Example: V={aaliyah_aardvark, …, zynga_zymgurgy} Thus…

17. Elementary Probability in Pictures A=1 A=2 A=3 A=4 A=5

18. Elementary Probability in Pictures A=aardvark A=aaliyah A=… A=…. A=zynga …

19. Probability - what you need to really, really know Probabilities are cool Random variables and events The Axioms of Probability Independence, binomials, multinomials Conditional probabilities Bayes Rule MLE’s, smoothing, and MAPs The joint distribution Inference Density estimation and classification

20. Definition of Conditional Probability P(A ^ B) P(A|B) = ----------- P(B) Corollary: The Chain Rule P(A ^ B) = P(A|B) P(B)

21. Some practical problems I have 3 standard d20 dice, 1 loaded die. Experiment: (1) pick a d20 uniformly at random then (2) roll it. Let A=d20 picked is fair and B=roll 19 or 20 with that die. What is P(B)? P(B) = P(B|A) P(A) + P(B|~A) P(~A) = 0.1*0.75 + 0.5*0.25 = 0.2 “marginalizing out” A

22. P(B|A) * P(A) P(B) P(A|B) = P(A|B) * P(B) P(A) P(B|A) = Bayes, Thomas (1763) An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, 53:370-418 …by no means merely a curious speculation in the doctrine of chances, but necessary to be solved in order to a sure foundation for all our reasonings concerning past facts, and what is likely to be hereafter…. necessary to be considered by any that would give a clear account of the strength of analogical or inductive reasoning… Bayes’ rule prior posterior

23. Some practical problems Joe throws 4 critical hits in a row, is Joe cheating? A = Joe using cheater’s die C = roll 19 or 20; P(C|A)=0.5, P(C|~A)=0.1 B = C1 and C2 and C3 and C4 Pr(B|A) = 0.0625 P(B|~A)=0.0001

24. What’s the experiment and outcome here? Outcome A: Joe is cheating Experiment: – Joe picked a die uniformly at random from a bag containing 10,000 fair die and one bad one. – Joe is a D&D player picked uniformly at random from set of 1,000,000 people and n of them cheat with probability p>0. – I have no idea, but I don’t like his looks. Call it P(A)=0.1 Moral of the story: with enough data, P(A) doesn’t really matter

25. Probability - what you need to really, really know Probabilities are cool Random variables and events The Axioms of Probability Independence, binomials, multinomials Conditional probabilities Bayes Rule MLE’s, smoothing, and MAPs The joint distribution Inference Density estimation and classification

26. Some practical problems I bought a loaded d20 on EBay…but it didn’t come with any specs. How can I find out how it behaves? 1. Collect some data (20 rolls) 2. Estimate Pr(i)=C(rolls of i)/C(any roll)

27. One solution I bought a loaded d20 on EBay…but it didn’t come with any specs. How can I find out how it behaves? P(1)=0 P(2)=0 P(3)=0 P(4)=0.1 … P(19)=0.25 P(20)=0.2 MLE = maximum likelihood estimate But: Do I really think it’s impossible to roll a 1,2 or 3? Would you bet your house on it?

28. A better solution I bought a loaded d20 on EBay…but it didn’t come with any specs. How can I find out how it behaves? 1. Collect some data (20 rolls) 2. Estimate Pr(i)=C(rolls of i)/C(any roll) 0. Imagine some data (20 rolls, each i shows up 1x)

29. A better solution I bought a loaded d20 on EBay…but it didn’t come with any specs. How can I find out how it behaves? P(1)=1/40 P(2)=1/40 P(3)=1/40 P(4)=(2+1)/40 … P(19)=(5+1)/40 P(20)=(4+1)/40=1/8 0.25 vs. 0.125 – really different! Maybe I should “imagine” less data?

30. A better solution? P(1)=1/40 P(2)=1/40 P(3)=1/40 P(4)=(2+1)/40 … P(19)=(5+1)/40 P(20)=(4+1)/40=1/8 0.25 vs. 0.125 – really different! Maybe I should “imagine” less data?

31. A better solution? Q: What if I used m rolls with a probability of q=1/20 of rolling any i? I can use this formula with m>20, or even with m<20 … say with m=1

32. A better solution Q: What if I used m rolls with a probability of q=1/20 of rolling any i? If m>>C(ANY) then your imagination q rules If m<<C(ANY) then your data rules BUT you never ever ever end up with Pr( i )=0

33. Terminology – more later This is called a uniform Dirichlet prior C(i), C(ANY) are sufficient statistics MLE = maximum likelihood estimate MAP= maximum a posteriori estimate

34. Probability - what you need to really, really know Probabilities are cool Random variables and events The Axioms of Probability Independence, binomials, multinomials Conditional probabilities Bayes Rule MLE’s, smoothing, and MAPs The joint distribution Inference Density estimation and classification

35. Using the Joint One you have the JD you can ask for the probability of any logical expression involving your attribute Abstract : Predict whether income exceeds $50K/yr based on census data. Also known as "Census Income" dataset. [Kohavi, 1996] Number of Instances: 48,842 Number of Attributes: 14 (in UCI’s copy of dataset); 3 (here)

36. Estimating the joint distribution Collect some data points Estimate the probability P(E1=e1 ^ … ^ En=en) as #(that row appears)/#(any row appears) …. GenderHoursWealth g1h1w1 g2h2w2..…… gNhNwN

37. Estimating the joint distribution For each combination of values r: – Total = C[ r ] = 0 For each data row r i – C[ r i ] ++ – Total ++ GenderHoursWealth g1h1w1 g2h2w2..…… gNhNwN Complexity? O(n) n = total size of input data O(2 d ) d = #attributes (all binary) = C[ r i ]/ Total r i is “female,40.5+, poor”

38. Estimating the joint distribution For each combination of values r: – Total = C[ r ] = 0 For each data row r i – C[ r i ] ++ – Total ++ GenderHoursWealth g1h1w1 g2h2w2..…… gNhNwN Complexity ? O(n) n = total size of input data k i = arity of attribute i

39. Estimating the joint distribution GenderHoursWealth g1h1w1 g2h2w2..…… gNhNwN Complexity? O(n) n = total size of input data k i = arity of attribute i For each combination of values r: – Total = C[ r ] = 0 For each data row r i – C[ r i ] ++ – Total ++

40. Estimating the joint distribution For each data row r i – If r i not in hash tables C,Total: Insert C[ r i ] = 0 – C[ r i ] ++ – Total ++ GenderHoursWealth g1h1w1 g2h2w2..…… gNhNwN Complexity? O(n) n = total size of input data m = size of the model O(m)

41. Bayes Classifiers If we can do inference over Pr(X 1 …,X d,Y)… … in particular compute Pr(X 1 …X d |Y) and Pr(Y). – And then we can use Bayes’ rule to compute

42. Copyright © Andrew W. Moore Summary: The Bad News Density estimation by directly learning the joint is trivial, mindless and dangerous Density estimation by directly learning the joint is hopeless unless you have some combination of Very few attributes Attributes with low “arity” Lots and lots of data Otherwise you can’t estimate all the row frequencies

43. Part of a Joint Distribution ABCDEp istheeffectofthe0.00036 istheeffectofa0.00034.Theeffectofthis0.00034 tothiseffect:“0.00034 betheeffectofthe… …………… nottheeffectofany0.00024 …………… doesnotaffectthegeneral0.00020 doesnotaffectthequestion0.00020 anymanneraffecttheprinciple0.00018

44. Probability - what you need to really, really know Probabilities are cool Random variables and events The Axioms of Probability Independence, binomials, multinomials Conditional probabilities Bayes Rule MLE’s, smoothing, and MAPs The joint distribution Inference Density estimation and classification Conditional Independence Naïve Bayes density estimators and classifiers

45. Copyright © Andrew W. Moore Naïve Density Estimation The problem with the Joint Estimator is that it just mirrors the training data. (and is also really, really hard to compute) We need something which generalizes more usefully. The naïve model generalizes strongly: Assume that each attribute is distributed independently of any of the other attributes.

46. Copyright © Andrew W. Moore Naïve Distribution General Case Suppose X 1,X 2,…,X d are independently distributed. So if we have a Naïve Distribution we can construct any row of the implied Joint Distribution on demand. How do we learn this?

47. Copyright © Andrew W. Moore Learning a Naïve Density Estimator Another trivial learning algorithm! MLE Dirichlet (MAP)

48. Can we make this interesting? Yes! Key ideas: – Pick the class variable Y – Instead of estimating P(X 1,…,X n,Y) = P(X 1 )*…*P(X n )*Y, estimate P(X 1,…,X n |Y) = P(X 1 |Y)*…*P(X n |Y) – Or, assume P(X i |Y)=Pr(X i |X 1,…,X i-1,X i+1,…X n,Y) – Or, that X i is conditionally independent of every X j, j!=i, given Y. – How to estimate? MLE or MAP

49. The Naïve Bayes classifier Dataset: each example has – A unique id id Why? For debugging the feature extractor – d attributes X 1,…,X d Each X i takes a discrete value in dom(X i ) – One class label Y in dom(Y) You have a train dataset and a test dataset

50. The Naïve Bayes classifier – v0 You have a train dataset and a test dataset Initialize an “event counter” (hashtable) C For each example id, y, x 1,….,x d in train: – C(“ Y =ANY”) ++; C(“ Y=y”) ++ – For j in 1..d : C(“ Y=y ^ X j =x j ”) ++ For each example id, y, x 1,….,x d in test: – For each y’ in dom(Y): Compute Pr (y’,x 1,….,x d ) = – Return the best y’

51. The Naïve Bayes classifier – v0 You have a train dataset and a test dataset Initialize an “event counter” (hashtable) C For each example id, y, x 1,….,x d in train: – C(“ Y =ANY”) ++; C(“ Y=y”) ++ – For j in 1..d : C(“ Y=y ^ X j =x j ”) ++ For each example id, y, x 1,….,x d in test: – For each y’ in dom(Y): Compute Pr (y’,x 1,….,x d ) = – Return the best y’

52. The Naïve Bayes classifier – v0 You have a train dataset and a test dataset Initialize an “event counter” (hashtable) C For each example id, y, x 1,….,x d in train: – C(“ Y =ANY”) ++; C(“ Y=y”) ++ – For j in 1..d : C(“ Y=y ^ X j =x j ”) ++ For each example id, y, x 1,….,x d in test: – For each y’ in dom(Y): Compute Pr (y’,x 1,….,x d ) = – Return the best y’ This may overfit, so …

53. The Naïve Bayes classifier – v1 You have a train dataset and a test dataset Initialize an “event counter” (hashtable) C For each example id, y, x 1,….,x d in train: – C(“ Y =ANY”) ++; C(“ Y=y”) ++ – For j in 1..d : C(“ Y=y ^ X j =x j ”) ++ For each example id, y, x 1,….,x d in test: – For each y’ in dom(Y): Compute Pr (y’,x 1,….,x d ) = – Return the best y’ where: q j = 1/|dom(X j )| q y = 1/|dom(Y)| m=1 This may underflow, so …

54. The Naïve Bayes classifier – v1 You have a train dataset and a test dataset Initialize an “event counter” (hashtable) C For each example id, y, x 1,….,x d in train: – C(“ Y =ANY”) ++; C(“ Y=y”) ++ – For j in 1..d : C(“ Y=y ^ X j =x j ”) ++ For each example id, y, x 1,….,x d in test: – For each y’ in dom(Y): Compute log Pr (y’,x 1,….,x d ) = – Return the best y’ where: q j = 1/|dom(X j )| q y = 1/|dom(Y)| m=1

55. The Naïve Bayes classifier – v2 For text documents, what features do you use? One common choice: – X 1 = first word in the document – X 2 = second word in the document – X 3 = third … – X 4 = … – … But: Pr( X 13 =hockey|Y=sports ) is probably not that different from Pr( X 11 =hockey|Y=sports )…so instead of treating them as different variables, treat them as different copies of the same variable

56. The Naïve Bayes classifier – v1 You have a train dataset and a test dataset Initialize an “event counter” (hashtable) C For each example id, y, x 1,….,x d in train: – C(“ Y =ANY”) ++; C(“ Y=y”) ++ – For j in 1..d : C(“ Y=y ^ X j =x j ”) ++ For each example id, y, x 1,….,x d in test: – For each y’ in dom(Y): Compute Pr (y’,x 1,….,x d ) = – Return the best y’

57. The Naïve Bayes classifier – v2 You have a train dataset and a test dataset Initialize an “event counter” (hashtable) C For each example id, y, x 1,….,x d in train: – C(“ Y =ANY”) ++; C(“ Y=y”) ++ – For j in 1..d : C(“ Y=y ^ X j =x j ”) ++ For each example id, y, x 1,….,x d in test: – For each y’ in dom(Y): Compute Pr (y’,x 1,….,x d ) = – Return the best y’

58. The Naïve Bayes classifier – v2 You have a train dataset and a test dataset Initialize an “event counter” (hashtable) C For each example id, y, x 1,….,x d in train: – C(“ Y =ANY”) ++; C(“ Y=y”) ++ – For j in 1..d : C(“ Y=y ^ X=x j ”) ++ For each example id, y, x 1,….,x d in test: – For each y’ in dom(Y): Compute Pr (y’,x 1,….,x d ) = – Return the best y’

59. The Naïve Bayes classifier – v2 You have a train dataset and a test dataset Initialize an “event counter” (hashtable) C For each example id, y, x 1,….,x d in train: – C(“ Y =ANY”) ++; C(“ Y=y”) ++ – For j in 1..d : C(“ Y=y ^ X=x j ”) ++ For each example id, y, x 1,….,x d in test: – For each y’ in dom(Y): Compute log Pr (y’,x 1,….,x d ) = – Return the best y’ where: q j = 1/|V| q y = 1/|dom(Y)| m=1