# Part III: Machine Learning. CS 188: Artificial Intelligence. Machine Learning This Set of Slides. Parameter Estimation. Estimation: Smoothing

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1 CS 188: Artificial Intelligence Lecture 20: Dynamic Bayes Nets, Naïve Bayes Pieter Abbeel UC Berkeley Slides adapted from Dan Klein. Part III: 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) Machine Learning This Set of Slides Parameter Estimation r g g r g g r g g r r g g g g An ML Example: Parameter Estimation Maximum likelihood Smoothing Applications Main concepts Naïve Bayes Estimating the distribution of a random variable Elicitation: ask a human (why is this hard?) Empirically: use training data (learning!) E.g.: 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 Issue: overfitting. E.g., what if only observed 1 jelly bean? Estimation: Smoothing Estimation: Laplace Smoothing Relative frequencies are the maximum likelihood estimates Laplace s estimate: Pretend you saw every outcome once more than you actually did In Bayesian statistics, we think of the parameters as just another random variable, with its own distribution???? Can derive this as a MAP estimate with Dirichlet priors (see cs281a) 1

2 Estimation: Laplace Smoothing Example: Spam Filter Laplace s estimate (extended): Pretend you saw every outcome k extra times What s Laplace with k = 0? k is the strength of the prior Laplace for conditionals: Smooth each condition independently: 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. Example: Digit Recognition Other Classification Tasks 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 ?? 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! Important Concepts Bayes Nets for Classification 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) Compute accuracy of test set Very important: never peek at the test set! Evaluation 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 We ll investigate overfitting and generalization formally in a few lectures Training Data Held-Out Data Test Data 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 2

3 Simple Classification Simple example: two binary features M General Naïve Bayes A general naive Bayes model: direct estimate S F Y x F n parameters Y Bayes estimate (no assumptions) Conditional independence Y parameters n x F x Y parameters F 1 F 2 F n + We only specify how each feature depends on the class Total number of parameters is linear in n Inference for Naïve Bayes Goal: compute posterior over causes Step 1: get joint probability of causes and evidence Step 2: get probability of evidence Step 3: renormalize + 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 Input: pixel grids A Digit Recognizer 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. Output: a digit 0-9 Here: lots of features, each is binary valued Naïve Bayes model: What do we need to learn? 3

4 Examples: CPTs Parameter Estimation Estimating distribution of random variables like X or X Y 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 Elicitation: ask a human! Usually need domain experts, and sophisticated ways of eliciting probabilities (e.g. betting games) Trouble calibrating A Spam Filter Naïve Bayes for Text 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. 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 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? Word at position i, not i th word in the dictionary! Example: Spam Filtering Spam Example Model: What are the parameters? ham : 0.66 spam: 0.33 the : to : and : of : you : a : with: from: Where do these tables come from? the : to : of : : with: from: and : a : 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) =

5 Example: Overfitting Example: Overfitting Posteriors determined by relative probabilities (odds ratios): south-west : inf nation : inf morally : inf nicely : inf extent : inf seriously : inf screens : inf minute : inf guaranteed : inf \$ : inf delivery : inf signature : inf 2 wins!! What went wrong here? 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 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 To generalize better: we need to smooth or regularize the estimates Estimation: Smoothing Estimation: Laplace Smoothing Relative frequencies are the maximum likelihood estimates Laplace s estimate: Pretend you saw every outcome once more than you actually did In Bayesian statistics, we think of the parameters as just another random variable, with its own distribution???? Can derive this as a MAP estimate with Dirichlet priors (see cs281a) 5

6 Estimation: Laplace Smoothing Estimation: Linear Interpolation Laplace s estimate (extended): Pretend you saw every outcome k extra times What s Laplace with k = 0? k is the strength of the prior Laplace for conditionals: Smooth each condition independently: 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? For even better ways to estimate parameters, as well as details of the math see cs281a, cs288 Real NB: Smoothing Tuning on Held-Out Data For real classification problems, smoothing is critical New odds ratios: 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 helvetica : 11.4 seems : 10.8 group : 10.2 ago : 8.4 areas : 8.3 verdana : 28.8 Credit : 28.4 ORDER : 27.2 <FONT> : 26.9 money : 26.5 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 Do these make more sense? 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 Confidences from a Classifier The confidence of a probabilistic classifier: Posterior over the top label Represents how sure the classifier is of the classification Any probabilistic model will have confidences No guarantee confidence is correct Calibration Weak calibration: higher confidences mean higher accuracy Strong calibration: confidence predicts accuracy rate What s the value of calibration? 6

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