CSE 473: Artificial Intelligence Autumn 2010

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1 CSE 473: Artificial Intelligence Autumn 2010 Machine Learning: Naive Bayes and Perceptron Luke Zettlemoyer Many slides over the course adapted from Dan Klein. 1

2 Outline Learning: Naive Bayes and Perceptron Naive Bayes models Parameter Estimation Smoothing Perceptron (binary and multi-class) Linear Ranking Models

3 Machine Learning Up until now: how to reason in a model and how to make optimal decisions Machine learning: how to acquire a model on the basis of data / experience Learning parameters (e.g. probabilities) Learning structure (e.g. BN graphs) Learning hidden concepts (e.g. clustering)

4 Example: Spam Filter Input: Output: spam/ham Setup: Get a large collection of example s, each labeled spam or ham Note: someone has to hand label all this data! Want to learn to predict labels of new, future s Features: The attributes used to make the ham / spam decision Words: FREE! Text Patterns: \$dd, CAPS Non-text: SenderInContacts Dear Sir. First, I must solicit your confidence in this transaction, this is by virture of its nature as being utterly confidencial and top secret. TO BE REMOVED FROM FUTURE MAILINGS, SIMPLY REPLY TO THIS MESSAGE AND PUT "REMOVE" IN THE SUBJECT. 99 MILLION ADDRESSES FOR ONLY \$99 Ok, Iknow this is blatantly OT but I'm beginning to go insane. Had an old Dell Dimension XPS sitting in the corner and decided to put it to use, I know it was working pre being stuck in the corner, but when I plugged it in, hit the power nothing happened.

5 Example: Digit Recognition Input: images / pixel grids Output: a digit 0-9 Setup: Get a large collection of example images, each labeled with a digit Note: someone has to hand label all this data! Want to learn to predict labels of new, future digit images Features: The attributes used to make the digit decision Pixels: (6,8)=ON Shape Patterns: NumComponents, AspectRatio, NumLoops 1??

6 Other Classification Tasks In classification, we predict labels y (classes) for inputs x Examples: Spam detection (input: document, classes: spam / ham) OCR (input: images, classes: characters) Medical diagnosis (input: symptoms, classes: diseases) Automatic essay grader (input: document, classes: grades) Fraud detection (input: account activity, classes: fraud / no fraud) Customer service routing many more Classification is an important commercial technology!

7 Important Concepts Data: labeled instances, e.g. s marked spam/ham Training set Held out set Test set Features: attribute-value pairs which characterize each x Experimentation cycle Learn parameters (e.g. model probabilities) on training set (Tune hyperparameters on held-out set) Very important: never peek at the test set! Evaluation Compute accuracy of test set Accuracy: fraction of instances predicted correctly Overfitting and generalization Want a classifier which does well on test data Overfitting: fitting the training data very closely, but not generalizing well Training Data Held-Out Data Test Data

8 Bayes Nets for Classification One method of classification: Use a probabilistic model! Features are observed random variables F i Y is the query variable Use probabilistic inference to compute most likely Y You already know how to do this inference

9 Simple Classification Simple example: two binary features M S F direct estimate Bayes estimate (no assumptions) Conditional independence +

10 General Naïve Bayes A general naive Bayes model: Y F 1 F 2 F n We only specify how each feature depends on the class Total number of parameters is linear in n

11 General Naïve Bayes What do we need in order to use naïve Bayes? Inference (you know this part) Start with a bunch of conditionals, P(Y) and the P(F i Y) tables Use standard inference to compute P(Y F 1 F n ) Nothing new here Estimates of local conditional probability tables P(Y), the prior over labels P(F i Y) for each feature (evidence variable) These probabilities are collectively called the parameters of the model and denoted by θ Up until now, we assumed these appeared by magic, but they typically come from training data: we ll look at this now

12 A Digit Recognizer Input: pixel grids Output: a digit 0-9

13 Naïve Bayes for Digits Simple version: One feature F ij for each grid position <i,j> Possible feature values are on / off, based on whether intensity is more or less than 0.5 in underlying image Each input maps to a feature vector, e.g. Here: lots of features, each is binary valued Naïve Bayes model: What do we need to learn?

14 Examples: CPTs

15 Parameter Estimation Estimating distribution of random variables like X or X Y Elicitation: ask a human! Usually need domain experts, and sophisticated ways of eliciting probabilities (e.g. betting games) Trouble calibrating Empirically: use training data For each outcome x, look at the empirical rate of that value: r g g This is the estimate that maximizes the likelihood of the data

16 A Spam Filter Naïve Bayes spam filter Data: Collection of s, labeled spam or ham Note: someone has to hand label all this data! Split into training, heldout, test sets Classifiers Learn on the training set (Tune it on a held-out set) Test it on new s Dear Sir. First, I must solicit your confidence in this transaction, this is by virture of its nature as being utterly confidencial and top secret. TO BE REMOVED FROM FUTURE MAILINGS, SIMPLY REPLY TO THIS MESSAGE AND PUT "REMOVE" IN THE SUBJECT. 99 MILLION ADDRESSES FOR ONLY \$99 Ok, Iknow this is blatantly OT but I'm beginning to go insane. Had an old Dell Dimension XPS sitting in the corner and decided to put it to use, I know it was working pre being stuck in the corner, but when I plugged it in, hit the power nothing happened.

17 Naïve Bayes for Text Bag-of-Words Naïve Bayes: Predict unknown class label (spam vs. ham) Assume evidence features (e.g. the words) are independent Warning: subtly different assumptions than before! Generative model Word at position i, not i th word in the dictionary! Tied distributions and bag-of-words Usually, each variable gets its own conditional probability distribution P(F Y) In a bag-of-words model Each position is identically distributed All positions share the same conditional probs P(W C) Why make this assumption?

18 Example: Spam Filtering Model: What are the parameters? ham : 0.66 spam: 0.33 the : to : and : of : you : a : with: from: the : to : of : : with: from: and : a : Where do these come from?

19 Spam Example Word P(w spam) P(w ham) Tot Spam Tot Ham (prior) Gary would you like to lose weight while you sleep P(spam w) = 98.9

20 Example: Overfitting 2 wins!!

21 Generalization and Overfitting Relative frequency parameters will overfit the training data! Just because we never saw a 3 with pixel (15,15) on during training doesn t mean we won t see it at test time Unlikely that every occurrence of minute is 100% spam Unlikely that every occurrence of seriously is 100% ham What about all the words that don t occur in the training set at all? In general, we can t go around giving unseen events zero probability As an extreme case, imagine using the entire as the only feature Would get the training data perfect (if deterministic labeling) Wouldn t generalize at all Just making the bag-of-words assumption gives us some generalization, but isn t enough To generalize better: we need to smooth or regularize the estimates

22 Estimation: Smoothing Problems with maximum likelihood estimates: If I flip a coin once, and it s heads, what s the estimate for P (heads)? What if I flip 10 times with 8 heads? What if I flip 10M times with 8M heads? Basic idea: We have some prior expectation about parameters (here, the probability of heads) Given little evidence, we should skew towards our prior Given a lot of evidence, we should listen to the data

23 Estimation: Smoothing Relative frequencies are the maximum likelihood estimates In Bayesian statistics, we think of the parameters as just another random variable, with its own distribution????

24 Estimation: Laplace Smoothing Laplace s estimate: Pretend you saw every outcome once more than you actually did H H T Can derive this as a MAP estimate with Dirichlet priors (Bayesian justfication)

25 Estimation: Laplace Smoothing Laplace s estimate (extended): Pretend you saw every outcome k extra times H H T What s Laplace with k = 0? k is the strength of the prior Laplace for conditionals: Smooth each condition independently:

26 Estimation: Linear Interpolation In practice, Laplace often performs poorly for P(X Y): When X is very large When Y is very large Another option: linear interpolation Also get P(X) from the data Make sure the estimate of P(X Y) isn t too different from P(X) What if α is 0? 1?

27 Tuning on Held-Out Data Now we ve got two kinds of unknowns Parameters: the probabilities P(Y X), P(Y) Hyperparameters, like the amount of smoothing to do: k, α Where to learn? Learn parameters from training data Must tune hyperparameters on different data Why? For each value of the hyperparameters, train and test on the held-out data Choose the best value and do a final test on the test data

28 Baselines First step: get a baseline Baselines are very simple straw man procedures Help determine how hard the task is Help know what a good accuracy is Weak baseline: most frequent label classifier Gives all test instances whatever label was most common in the training set E.g. for spam filtering, might label everything as ham Accuracy might be very high if the problem is skewed E.g. calling everything ham gets 66%, so a classifier that gets 70% isn t very good For real research, usually use previous work as a (strong) baseline

29 Precision vs. Recall Let s say we want to classify web pages as homepages or not In a test set of 1K pages, there are 3 homepages Our classifier says they are all non-homepages 99.7 accuracy! Need new measures for rare positive events - guessed + actual + Precision: fraction of guessed positives which were actually positive Recall: fraction of actual positives which were guessed as positive Say we detect 5 spam s, of which 2 were actually spam, and we missed one Precision: 2 correct / 5 guessed = 0.4 Recall: 2 correct / 3 true = 0.67 Which is more important in customer support automation?

30 Precision vs. Recall Precision/recall tradeoff Often, you can trade off precision and recall Only works well with weakly calibrated classifiers To summarize the tradeoff: Break-even point: precision value when p = r F-measure: harmonic mean of p and r:

32 What to Do About Errors? Need more features words aren t enough! Have you ed the sender before? Have 1K other people just gotten the same ? Is the sending information consistent? Is the in ALL CAPS? Do inline URLs point where they say they point? Does the address you by (your) name? Can add these information sources as new variables in the NB model Next class we ll talk about classifiers which let you easily add arbitrary features more easily

33 Summary Bayes rule lets us do diagnostic queries with causal probabilities The naïve Bayes assumption takes all features to be independent given the class label We can build classifiers out of a naïve Bayes model using training data Smoothing estimates is important in real systems Classifier confidences are useful, when you can get them

35 What to Do About Errors? Need more features words aren t enough! Have you ed the sender before? Have 1K other people just gotten the same ? Is the sending information consistent? Is the in ALL CAPS? Do inline URLs point where they say they point? Does the address you by (your) name? Can add these information sources as new variables in the NB model Next class we ll talk about classifiers which let you easily add arbitrary features more easily

36 Summary Bayes rule lets us do diagnostic queries with causal probabilities The naïve Bayes assumption takes all features to be independent given the class label We can build classifiers out of a naïve Bayes model using training data Smoothing estimates is important in real systems Classifier confidences are useful, when you can get them

37 Generative vs. Discriminative Generative classifiers: E.g. naïve Bayes A joint probability model with evidence variables Query model for causes given evidence Discriminative classifiers: No generative model, no Bayes rule, often no probabilities at all! Try to predict the label Y directly from X Robust, accurate with varied features Loosely: mistake driven rather than model driven

38 Some (Simplified) Biology Very loose inspiration: human neurons

39 Linear Classifiers Inputs are feature values Each feature has a weight Sum is the activation If the activation is: Positive, output +1 Negative, output -1 f 1 f 2 f 3 w 1 w 2 Σ >0? w 3

40 Example: Spam Imagine 4 features (spam is positive class): free (number of occurrences of free ) money (occurrences of money ) BIAS (intercept, always has value 1) free money BIAS : 1 free : 1 money : 1... BIAS : -3 free : 4 money : 2...

41 Binary Decision Rule In the space of feature vectors Examples are points Any weight vector is a hyperplane One side corresponds to Y=+1 Other corresponds to Y=-1 money 2 +1 = SPAM BIAS : -3 free : 4 money : = HAM free

42 Binary Perceptron Algorithm Start with zero weights For each training instance: Classify with current weights If correct (i.e., y=y*), no change! If wrong: adjust the weight vector by adding or subtracting the feature vector. Subtract if y* is -1.

43 Examples: Perceptron Separable Case

44 Examples: Perceptron Inseparable Case

45 Multiclass Decision Rule If we have more than two classes: Have a weight vector for each class: Calculate an activation for each class Highest activation wins

46 Example win the vote win the election win the game BIAS : win : game : vote : the :... BIAS : win : game : vote : the :... BIAS : win : game : vote : the :...

47 Example win the vote BIAS : 1 win : 1 game : 0 vote : 1 the : 1... BIAS : -2 win : 4 game : 4 vote : 0 the : 0... BIAS : 1 win : 2 game : 0 vote : 4 the : 0... BIAS : 2 win : 0 game : 2 vote : 0 the : 0...

48 The Multi-class Perceptron Alg. Start with zero weights Iterate training examples Classify with current weights If correct, no change! If wrong: lower score of wrong answer, raise score of right answer

49 Mistake-Driven Classification For Naïve Bayes: Parameters from data statistics Parameters: probabilistic interpretation Training: one pass through the data Training Data For the perceptron: Parameters from reactions to mistakes Parameters: discriminative interpretation Training: go through the data until heldout accuracy maxes out Held-Out Data Test Data

50 Properties of Perceptrons Separability: some parameters get the training set perfectly correct Separable Convergence: if the training is separable, perceptron will eventually converge (binary case) Mistake Bound: the maximum number of mistakes (binary case) related to the margin or degree of separability Non-Separable

51 Problems with the Perceptron Noise: if the data isn t separable, weights might thrash Averaging weight vectors over time can help (averaged perceptron) Mediocre generalization: finds a barely separating solution Overtraining: test / held-out accuracy usually rises, then falls Overtraining is a kind of overfitting

52 Fixing the Perceptron Idea: adjust the weight update to mitigate these effects MIRA*: choose an update size that fixes the current mistake but, minimizes the change to w The +1 helps to generalize * Margin Infused Relaxed Algorithm

53 Minimum Correcting Update min not τ=0, or would not have made an error, so min will be where equality holds

54 Maximum Step Size In practice, it s also bad to make updates that are too large Example may be labeled incorrectly You may not have enough features Solution: cap the maximum possible value of τ with some constant C Corresponds to an optimization that assumes non-separable data Usually converges faster than perceptron Usually better, especially on noisy data

55 Linear Separators Which of these linear separators is optimal?

56 Support Vector Machines Maximizing the margin: good according to intuition, theory, practice Only support vectors matter; other training examples are ignorable Support vector machines (SVMs) find the separator with max margin Basically, SVMs are MIRA where you optimize over all examples at once MIRA SVM

57 Classification: Comparison Naïve Bayes Builds a model training data Gives prediction probabilities Strong assumptions about feature independence One pass through data (counting) Perceptrons / MIRA: Makes less assumptions about data Mistake-driven learning Multiple passes through data (prediction) Often more accurate

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