- 1. Machine Learning with Applications in Categorization, Popularity and Sequence labeling (linear models, decision trees, ensemble methods, evaluation) Dr. Nicolas Nicolov <1st_last@yahoo.com>
- 2. Goals • Introduce important ML concepts • Illustrate ML techniques through examples in: – Categorization – Popularity – Sequence labeling (tutorial aims to be self-contained and to explain the notation) 2
- 3. Outline • Examples of applications of Machine Learning • Encoding objects with features • The Machine Learning framework • Linear models – Perceptron, Winnow, Logistic Regression, Robust Risk Minimization (RRM) • Tree models (Decision Trees DTs) – Classification Decision Trees, Regression Trees • Boosting – AdaBoost • Ranking evaluation – Kendall tau and Spearman’s coefficient • Sequence labeling – Hidden Markov Models (HMMs) 3
- 4. EXAMPLES OF MACHINE LEARNING Why?– Get a flavor of the diversity of areas where ML is applied. 4
- 5. Sequence Labeling (like search query analysis) Geo-Political Entity PER_ _PER_ _PER X GPE George W. Bush discussed Iraq <PER>George W. Bush</PER> discussed <GPE>Iraq</GPE> George W. Bush discussed Iraq 5
- 6. Spam www.dietsthatwork.com www . dietsthatwork . com further segmentation www . diets that work . com classification SPAM! 6
- 7. Tokenization What!?I love the iphone:-) What !? I love the iphone :-) How difficult can that be? — 98.2% [Zhang et al. 2003] NO TRESSPASSING VIOLATORS WILL BE PROSECUTED 7
- 8. NL Parsing syntactic structure PREP CONTR DOBJ MANR POSS SUBJ DET DET MOD MOD MOD Unlike my sluggish Chevy the Audi handles the winding mountain roads superbly 8
- 9. State Transitions LEFTARC: λ λ β RIGHTARC: λ λ β λ β NOARC: λ λ β SHIFT: using ML to make the decision λ λ β which action to take 9
- 10. Two Ladies in a Men’s Club 10
- 11. SUBJ IOBJ We serve men We serve food to men. We serve our community. serve —IndirectObject men SUBJ DOBJ We serve men We serve organic food. We serve coffee to connoiseurs. serve —DirectObject men 11
- 12. Coreference Audi is an automaker that makes luxury cars and SUVs. The company was born in Germany . It was established by August Horch in 1910. Horch had previosly founded another company and his models were quite popular. Audi started with four cylinder models. By 1914, Horch 's new cars were racing and winning. August Horch left the Audi company in 1920 to take a position as an industry representative for the German motor vehicle industry federation. Currently Audi is a subsidiary of the Volkswagen group and produces cars of outstanding quality. 12
- 13. Parts of Objects (Meronymy) […] the interior seems upscale with leatherette upholstery that looks and feels better than the real cow hide found in more expensive vehicles, a dashboard accented by textured soft-touch materials, a woven mesh headliner, and other materials that give the New Beetle’s interior a sense of quality. […] Finally, and a big plus in my book, both front seats were height adjustable, and the steering column tilted and telescoped for optimum comfort. 13
- 14. Sentiment Analysis Positive Negative Xbox Xbox I love pineapple nearly as much as I hate bananas. POSITIVE sentiment regarding topic pineapple. 14
- 15. Chinese Sentiment Sentence Car aspects Sentiment categories 15
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- 18. Categorization • High-level task: – Given a restaurant what is its restaurant sub-category? • Encoding entities with features • Feature selection non-standard order • Linear models “Though this be madness, yet there is method in't.” 18
- 19. Roadmap • Examples of applications of Machine Learning • Encoding objects with features • The Machine Learning framework • Linear models – Perceptron, Winnow , Logistic Regression, Robust Risk Minimization (RRM) • Tree models (Decision Trees DTs) – Classification Decision Trees, Regression Trees • Boosting – AdaBoost • Ranking evaluation – Kendall tau and Spearman’s coefficient • Sequence labeling – Hidden Markov Models (HMMs) 19
- 20. ENCODING OBJECTS WITH FEATURES Why?– ML algorithms are “generic”; most of them are cast as solutions around vector encodings of the domain objects. Regardless of the ML algorithm we will need to represent/encode the domain objects as feature vectors. How well we do this (the quality of features) directly impacts system performance. 20
- 21. Flat Object Encoding Can be a set; object can belong Number of to several classes. features can be millions. 37 1 0 0 1 1 1 0 1 … Machine learning (training) instance/example/observation. 21
- 22. Structured Objects to Strings to Features Table can be quite large. Structured object: Feature string Feature index Read as field “f2:f4” contains feature “a”. *DEFAULT* 0 f1 … … f2 f2:f4>a 100 f4 abcde “f2:f4>a” f2:f4>b 101 f5 “f2:f4>b” uni-grams “f2:f4>c” f2:f4>c 102 f3 … … … f6 “f2:f4>a_b” f2:f4>a_b 105 “f2:f4>b_c” bi-grams “f2:f4>c_d” f2:f4>b_c 106 … f2:f4>c_d 107 “f2:f4>a_b_c” tri-grams … … “f2:f4>b_c_d” f2:f4>a_b_c 109 22
- 23. Sliding Window (bi-grams) SkyCity at the Space Needle add initial “^” and final “$” tokens ^ SkyCity at the Space Needle $ sliding window ^ SkyCity at the Space Needle $ ^ SkyCity at the Space Needle $ ^ SkyCity at the Space Needle $ ^ SkyCity at the Space Needle $ 23
- 24. Example: Feature Templates public static List<string> NGrams( string field ) could add field name as argument and prefix all features { var featutes = new List<string>(); string[] tokens = field.Split( spaceCharArr, System.StringSplitOptions.RemoveEmptyEntries ); featutes.Add( string.Join( "", field.Split(SPLIT_CHARS) ) ); // the entire field string unigram = string.Empty, bigram, previous1 = "^", previous2 = "^", trigram; for (int i = 0; i < tokens.Length; i++) { unigram = tokens[ i ]; featutes.Add(unigram); bigram = previous1 + "_" + unigram; initial bigram is “^_tokens*0]" featutes.Add( bigram ); if ( i >= 1 ) { trigram = previous2 + "_" + bigram; featutes.Add( trigram ); } previous2 = previous1; initial tri-gram is: "^_tokens[0]_tokens[1] " previous1 = unigram; } featutes.Add( unigram + "_$" ); featutes.Add( bigram + "_$" ); last trigram is “tokens*tokens.Length-2]_tokens[tokens.Length-1]_$" return result; 24 }
- 25. The Art of Feature Engineering: Disjunctive Features • Useful feature = triggers often and with a particular class. • Rarely occurring (but indicative of a class) features can be combined in a disjunction. This results in: – Need for less data to achieve good performance. – Final system performance (with all available data) is higher. • How can we get insights about such features: Error analysis! Regex ITALIAN_FOOD = new Regex(@"al dente|agnello|alfredo|antipasti|antipasto|arrabbiata|bistecca|bolognese| branzino|caprese|carbonara|carpaccio|cioppino|cozze|fettuccine|filetto|focaccia|frutti di mare|funghi| gnocchi|gorgonzola|insalata|lasagna|linguine|linguini|macaroni|minestrone|mozzarella|ossobuco|panini| panino| parmigiana|pasticcio|pecorino|penne|pepperoncini|pesce|pesto|piatti|piatto|piccata|polpo|pomodori|prosciutto| radicchio|ravioli|ricotta|rigatoni|risotto|saltimbocca|scallopini|scaloppini|spaghetti|tagliatelle|tiramisu| tortellini|vitello|vongole"); if (ITALIAN_FOOD.Match(entity.description).Success) features.Add("Italian_Food_Matched_Description"); Triggering of the feature. Up to us how we call the feature. 25
- 26. Generic Nature of ML Systems human sees Indices of (binary) features that trigger. instance( class= 7, features=[0,300857,100739,200441,...]) computer “sees” instance( class=99, features=[0,201937,196121,345758,13,...]) instance( class=42, features=[0,99173,358387,1001,1,...]) ... Number of features that trigger for individual instances are often not the same. 26 Default feature always triggers.
- 27. Training Data Instance /w outcome. 27
- 28. Feature Selection • Templates: powerful way to get lots of features. • We get too many features. e.g., 20M for dependency parsing. • Danger of overfitting. Doing well on seen data but poorly on unseen data. • Feature selection: Automatic ways of finding discriminative features. – CountCutOff. – TFxIDF. – Mutual information. – Information gain. – Chi square. We will examine in detail the implementation of this. 28
- 29. Mutual Information 29
- 30. Information Gain Balances effects of feature triggering for an object with the effects of feature being absent for an object. 30
- 31. Chi Square float Chi2(int a, int b, int c, int d) { return (a+b+c+d)* ((a*d-b*c)^2) / ((a+b)*(a+c)*(c+d)*(b+d)); } 31
- 32. Exponent(Log) Trick While the final output may not be big intermediate results are. Solution: float Chi2(int a, int b, int c, int d) { return (a+b+c+d) * ((a*d-b*c)^2) / ((a+b)*(a+c)*(c+d)*(b+d)); } float Chi2_v2(int a, int b, int c, int d) { double total = a + b + c + d; double n = Math.Log(total); double num = 2.0 * Math.Log(Math.Abs((a * d) - (b * c))); double den = Math.Log(a + b) + Math.Log(a + c) + Math.Log(c + d) + Math.Log(b + d); return (float) Math.Exp(n+num-den); 32 }
- 33. Chi Square: Score per Feature 33
- 34. Chi Square Feature Selection int[] featureCounts = new int[ numFeatures ]; int numLabels = labelIndex.Count; int[] classTotals = new int[ numLabels ]; // instances with that label. float[] classPriors = new float[ numLabels ]; // class priors: classTotals[label]/numInstances. int[,] counts = new int[ numLabels, numFeatures ]; // (label,feature) co-occurrence counts. int numInstances = instances.Count; ... Do a pass over the data and collect above counts. float[] weightedChiSquareScore = new float[ numFeatures ]; for (int f = 0; f < numFeatures; f++) // f is a feature index { float score = 0.0f; for (int labelIdx = 0; labelIdx < numLabels; labelIdx++) { int a = counts[ labelIdx, f ]; int b = classTotals[ labelIdx ] - p; int c = featureCounts[ f ] - p; int d = numInstances - ( p + q + r ); if (p >= MIN_SUPPORT && q >= MIN_SUPPORT) { // MIN_SUPPORT = 5 score += classPriors[ labelIdx ] * Chi2( a, b, c, d ); } } Weighted average across all classes. weightedChiSquareScore[ f ] = score; } 34
- 35. ⇒ Summary: Encoding • Object representation is crucial. • Humans: good at suggesting features (templates). • Computers: good at filtering (feature selection). The system designer does not have to worry about which feature is more important or useful, and the job is left to the learning algorithm to assign appropriate weights to the corresponding features. The system designer’s job is to define a set of features that is large enough to represent most of the useful information, yet small enough to be manageable for the algorithms and the infrastructure. • Feature engineering: Ensuring systems use the “right” features. 35
- 36. Roadmap • Examples of applications of Machine Learning • Encoding objects with features • The Machine Learning framework • Linear models – Perceptron, Winnow , Logistic Regression, Robust Risk Minimization (RRM) • Tree models (Decision Trees DTs) – Classification Decision Trees, Regression Trees • Boosting – AdaBoost • Ranking evaluation – Kendall tau and Spearman’s coefficient • Sequence labeling – Hidden Markov Models (HMMs) 36
- 38. Machine Learning: Representation Complex decision making: prediction (response/dependent variable). input/independent variable Can be qualitative/quantitative (classification/regression). classifier 38
- 39. Notation 39
- 40. Machine Learning object encoded with features Offline Online Training Model classifier System Sub-system TRAINING prediction (response/dependent variable) 40
- 41. Classes of Learning Problems • Classification: Assign a category to each item (Chinese | French | Indian | Italian | Japanese restaurant). • Regression: Predict a real value for each item (stock/currency value, temperature). • Ranking: Order items according to some criterion (web search results relevant to a user query). • Clustering: Partition items into homogeneous groups (clustering twitter posts by topic). • Dimensionality reduction: Transform an initial representation of items into a lower-dimensional representation while preserving some properties (preprocessing of digital images). 41
- 42. ML Terminology • Examples: Items or instances used for learning or evaluation. • Features: Set of attributes represented as a vector associated with an example. • Labels: Values or categories assigned to examples. In classification the labels are categories; in regression the labels are real numbers. • Target: The correct label for a training example. This is extra data that is needed for supervised learning. • Output: Prediction label from input set of features using a model of the machine learning algorithm. • Training sample: Examples used to train a machine learning algorithm. • Validation sample: Examples used to tune parameters of a learning algorithm. • Model: Information that the machine learning algorithm stores after training. The model is used when predicting the output labels of new, unseen examples. • Test sample: Examples used to evaluate the performance of a learning algorithm. The test sample is separate from the training and validation data and is not made available in the learning stage. • Loss function: A function that measures the difference/loss between a predicted label and a true label. We will design the learning algorithms so that they minimize the error (cumulative loss across all training examples). • Hypothesis set: A set of functions mapping features (feature vectors) to the set of labels. The learning algorithm chooses one function among those in the hypothesis set to return after training. Usually we pick a class of functions (e.g., linear functions) parameterized by a set of free parameters (e.g., coefficients of the linear function) and pinpoint the final hypothesis by identifying the parameters that minimize the error. • Model selection: Process for selecting the free parameters of the algorithm (actually of the function in the hypothesis set). 42
- 43. Classification Yes, this is mysterious at this point. + − + + − − + − + + − − − + − + + − − + − + − − 43 decision boundary
- 44. Multi-Class Classification 44
- 45. One-Versus-All (OVA) For each category in turn, create a binary classifier where an instance in the data belonging to the category is considered a positive example, all other examples are considered negative examples. Given a new object, run all these binary classifiers and see which classifier has the “highest prediction”. The scores from the different classifiers need to be calibrated! 45
- 46. One-Versus-One (OVO) For each pair of classes, create binary classifier on data labeled as either of the classes. How many such classifiers? Given a new instance run all classifiers and predict class with maximum number of wins. 46
- 47. Errors “Nobody is perfect, but then again, who wants to be nobody.” Average error across all instances. Goal: Minimize the Error. Beneficial to have differentiable loss function. #misclassified examples (penalty score of 1 for every misclassified example). 47
- 48. Error: Function of the Parameters The cumulative error across all instances is a function of the parameters. 1 2 48
- 49. Evaluation • Motivation: – Benchmark algorithms (which system is better). – Tuning parameters during training. 49
- 50. Evaluation Measures GeneralizationError: Probability to misclassify an instance selected according to the distribution of the labeled instance space TrainingError: Percentage of training examples which are correctly classified. Optimistically biased estimate especially if the inducer over-fits the (training) data. Empirical estimation of the generalization error: • Heldout method • Re-sampling: 1. Random resampling 2. Cross-validation 50
- 51. Precision, Recall and F-measure General Setup Let’s consider binary classification: Space of all instances System identified these as negative and got them correct (true negative). System identified these as positive System identified System identified but got them these as positive these as negative wrong but got them but got them (false positive). correct wrong (true positive). (false negative). Instances identified as Positive instances in reality. positive by the system. 51
- 52. Definitions Accuracy, Precision, Recall, and F-measure TN: true negatives Precision: FP: false positives TP: true positives FN: false negatives Recall: F-measure: Harmonic mean of Accuracy: precision and recall 52
- 53. Accuracy vs. Prec/Rec/F-meas Accuracy can be misleading for evaluating a model with an imbalanced distribution of the class. When there are more majority class instances than minority class instances, predicting always the majority class gives good accuracy. Precision and recall (together) are better indicators. As a single, aggregate number f-measure favors the lower of the precision or recall. 53
- 54. Extreme Cases for Precision & Recall all instances TN: true negatives TP: true positive FN: false negatives system actual all instances system FP: false positives TP: true positives Precision can be traded for recall and vice 54 versa. actual
- 55. Definitions Sensitivity & Specificity TN: true negatives FP: false positives [same as recall; TP: aka true positive rate] true positives FN: false negatives [aka true negative rate] False positive rate: False negative rate: 55
- 56. Venn Diagrams These visualization diagrams were introduced by John Venn: John Venn (1880) “On the Diagrammatic and Mechanical Representation of Propositions and Reasonings”, Philosophical Magazine and Journal of Science, 5:10(59). What if there are three classes? Four classes? With more classes our visual intuitions are helping less and less. A subtle point: These are just the actual/real classes without the system Six classes? classes drawn on top! 56
- 57. Confusion Matrix Shows how the predictions of instances of an actual class are distributed across all classes. Here is an example confusion matrix for three classes: Predicted class A Predicted class B Predicted class C Number of instances Number of instances in the actual class A in the actual class A Total number of actual Actual class A … AND predicted as BUT predicted as instances of class A belonging to class A. belonging to class B. Total number of actual Actual class B … … … instances of class B Total number of actual Actual class C … … … instances of class C Total number of Total number of Total number of Total number of instances instances predicted instances predicted instances predicted as class A as class B as class C Counts on the diagonal are the true positives for each class. Counts not on the diagonal are errors. Confusion matrices can handle many classes. 57
- 58. Confusion Matrix: Accuracy, Precision and Recall Given a confusion matrix, it’s easy to compute accuracy, precision and recall: Predicted class A Predicted class B Predicted class C Actual class A 50 80 70 200 Actual class B 40 140 120 300 Actual class C 120 220 160 500 210 440 350 1000 58 Confusion matrices can, themselves, be confusing sometimes
- 59. Roadmap • Examples of applications of Machine Learning • Encoding objects with features • The Machine Learning framework • Linear models – Perceptron, Winnow , Logistic Regression, Robust Risk Minimization (RRM) • Tree models (Decision Trees DTs) – Classification Decision Trees, Regression Trees • Boosting – AdaBoost • Ranking evaluation – Kendall tau and Spearman’s coefficient • Sequence labeling – Hidden Markov Models (HMMs) 59
- 60. LINEAR MODELS Why?– Linear models are good way to learn about core ML concepts. 60
- 61. Refresher: Vectors vector vector point point vector points are also vectors. Equation of the line. Can be re-written as: sum of vectors vector notation 61 transpose
- 62. Refresher: Vectors (2) Equation of the line. Can be re-written as: Normal vector. vector notation 62
- 63. Refresher: Dot Product float DotProduct(float[] v1, float[] v2) { float sum = 0.0; for(int i=0; i< a.Length; i++) sum+= v1[i] * v2[i]; return sum; } 63
- 64. Refresher: Pos/Neg Classes + Normal vector. − 64
- 65. sgn Function In mathematics: We will use: Informally drawn as: 65
- 66. Two Linear Models The features of an object have associated weights indicating their importance. Signal: Perceptron Linear regression 66
- 67. Why “Regression”? Why the term for quantitative output prediction is “regression”? “That same year [1875], [Francis] Galton decided to recruit some of his friends for an experiment with sweet peas. He distributed seeds among seven of them, asking them to plant the seeds and return the offspring. Galton measured the baby seeds and compared their diameters to those of their parents. He noticed a phenomenon that initially seems counter-intuitive: the large seeds tended to produce smaller offspring, and the small seeds tended to produce larger offspring. A decade later he analyzed data from his anthropometric laboratory and recognized the same pattern with human heights. After measuring 205 pairs of parents and their 928 adult children, he saw that exceptionally tall parents had kids who were generally shorter than they were, while exceptionally short parents had children who were generally taller than their parents. After reflecting upon this, we can understand why it must be the case. If very tall parents always produced even taller children, and if very short parents always produced even shorter ones, we would by now have turned into a race of giants and midgets. Yet this hasn't happened. Human populations may be getting taller as a whole – due to better nutrition and public health – but the distribution of heights within the population is still contained. Galton called this phenomenon ‘regression towards mediocrity in hereditary stature’. The concept is now more generally known as regression to the mean.” [A.Bellos pp.375] 67
- 68. On-Line (Sequential) Learning • On-line = process one example at a time. • Attractive for large scale problems. iteration (epoch/time). Compute loss. Objective: Minimize cumulative loss: return parameters 68
- 69. On-Line (Sequential) Learning (2) Sometimes written out more explicitly: # passes over the data. for each data item. return parameters return parameters 69
- 70. Perceptron Linearly separable data: Non-linearly separable data: + − + + − + + − + + − − − + + + − − + + + + − + − + − − + − − + − + − + − + − − − − + − + − − − + − − + − − 70
- 71. First: Perceptron Update Rule Simplification: Lines pass through origin. + + − + − 71
- 73. Perceptron Learning Algorithm iteration (epoch/time). return parameters 73
- 74. Perceptron Learning Algorithm (algorithm makes multiple passes over data.) return parameters 74
- 75. Perceptron Learning Algorithm (PLA) while( mis-classified examples exist ): Misclassified example means: With the current weights Update weights: 1. A challenge: Algorithm will not terminate for non-linearly separable data (outliers, noise). 2. Unstable: jump from good perceptron to really bad one within one update. 3. Attempting to minimize: NP-hard. 75
- 76. Perceptron Weight update: 76
- 77. Looks Simple – Does It Work? Margin-based upper bound on updates: Number of updates by the Perceptron Algorithm Fact: where: Remarkable: Does not depend on dimension of feature space! 77
- 78. Compact Model Representation Use float instead of double: Store only non-zero weights (and indices): Store non-zero weights and diff of indices: void Save( StreamWriter w, int labelIdx, float[] weights ) { w.Write( labelIdx ); int previousIndex = 0; for (int i = 0; i < weights.Length; i++) Difference of indices. { if (weights[ i ] != 0.0f) { w.Write( " " + (i - previousIndex) + " " + weights[ i ] ); previousIndex = i; } } Remember last index where the weight was non-zero . } 78
- 79. Linear Classification Solutions Different solutions (infinitely many) + − + + − − + − + + − + − − − + + − − + − + − − 79
- 80. The Pocket Algorithm A better perceptron algorithm: Keep track of the error and update weights when we lower the error. Compute error. Expensive step! Access to the entire data needed! Only update the best weights if we lower the error! 80
- 81. Voted Perceptron • Training as the usual perceptron algorithm (with some extra book-keeping). • Decision rule: iterations 81
- 82. Dual Perceptron: Intuitions + + + + + + + separating line. + + normal vector − − − − − − − − 82
- 83. Dual Perceptron (algorithm makes multiple passes over data.) return parameters Decision rule: 83
- 84. Exclusive OR (XOR) Function Truth table: Inputs in and color-coding of the output: Challenge: The data is not linearly separable ??? (no straight line can be drawn that separates the green from the blue points). 84
- 85. Solution for the Exclusive OR (XOR) We introduce another input dimension: Now the data is linearly separable: 85
- 86. Winnow Algorithm iteration (epoch). Normalizing constant. Multiplicative update. return parameters 86
- 87. Training, Test Error and Complexity Test error Training error Model complexity 87
- 88. Logistic Regression Target: Data does not give the probability explicitly: Logistic function: 88
- 89. Logistic Regression Data likelihood: Negative log-likelihood: Error: 89
- 90. Derivative: Refresher Chain rule: Partial derivative: Gradient (derivatives with respect to each component): This is a vector and we Gradient of the error: can compute it at a point. 90
- 91. Hypothesis Space Weight space/hyperplane. 91 [graph from T.Mitchell]
- 92. Math Fact The gradient of the error: (a vector in weight space) specifies the direction of the argument that leads to the steepest increase for the value of the error. The negative of the gradient gives the direction of the steepest decrease. Negative gradient (see next slides). 92
- 93. Computing the Gradient Because gradient is a linear operator. 93
- 94. (Batch) Gradient Descent repeat Compute gradient: Update weights: max #iterations; marginal error improvement; and small value for the error. 94
- 95. Punch Line The new object is in the class if: classification rule. 95
- 96. Newton’s Method 2.5 2 1.5 1 0.5 0 0 0.5 1 1.5 2 2.5 3 -0.5 96
- 97. Newton-Raphson 97
- 98. Robust Risk Minimization Notation: input vector label training examples weight vector bias continuous linear model Prediction rule: Classification error: 98
- 99. Robust Classification Loss Parameter estimation: Hinge loss: Robust classification loss: 99
- 100. Loss Functions: Comparison 100
- 101. Confidence and Regularization Confidence Regularization: Unconstrained optimization (Lagrange multiplier): smaller λ corresponds to a larger A. 101
- 102. Robust Risk Minimization Go over the training data. 102
- 103. Learning Curve 100 • Plots evaluation metric 90 Experiment with 50% of against fraction of the training data yields training data (on the 80 evaluation number of 70. same test set!). 70 • Highest performance bounded by human inter 60 annotator agreement 50 (ITA). • Leveling off effect that 40 can guide us how much 30 data is needed. 20 10 0 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Percentage of data used for each experiment. 103
- 104. Summary • Examples of ML • Categorization • Object encoding • Linear models: – Perceptron – Winnow – Logistic Regression – RRM • Engineering aspects of ML systems 104
- 105. PART II: POPULARITY 105
- 106. Goal • Quantify how popular an entity is. Motivation: • Used in the new local search relevance metric. 106
- 107. What is popularity? 107
- 108. POPULARITY IN LOCAL SEARCH 108
- 109. Popularity • Output a popularity score (regression) • Ensemble methods • Tree base procedure (non-linear) • Boosting 109
- 110. When is a Local Entity Popular? • Definition: Visited by many people in the context of alternative choices. • Is the popularity of restaurants the same as the popularity of movies, etc.? • How to operationalize “visit”, “many”, “alternative choices”? – Initially we are using: popular means clicked more. • Going forward we will use: – “visit” = click given an impression. – “choice” = density of entities in the same primary category. – “many” = fraction of clicks from impressions. 110
- 111. Local Entity Popularity The model then will be regression: 111
- 112. Not all Clicks are Born the Same • Click in the context of a named query: – Can even be argued we are not satisfying the user information needs (and they have to click further to find out what they are looking for). • Click in the context of a category query: – Much more significant (especially when alternative results are present). 112
- 113. Local Entity Popularity • Popularity & 1st page , current ranker. • Entities without URL. • Newly created entities. • Clicks vs. mouseovers. • Scenario: 50 French restaurants; best entity has 2k clicks. 2 Italian restaurants; best entity has 2k clicks. The French entity is more popular because of higher available choice. 113
- 114. Entity Representation 9000 8000 … 4000 65 4.7 73 … 1 … Target feature values Machine learning (training) instance 114
- 115. POISSON REGRESSION Why?– We will practice the ML machinery on a different problem, re-iterating the concepts. Poisson regression is an example of log-linear models good for modeling counts (e.g., number of visitors to a store in a certain time). 115
- 116. Setup explanatory variables response/outcome variable These counts for our scenario are the clicks on the web page. A good way to model counts of observations is using the Poisson distribution. 116
- 117. Poisson Distribution: Preliminaries The Poisson distribution realistically describes the pattern of requests over time in many client-server situations. Examples are: incoming customers at a bank, calls into a company’s telephone exchange, requests for storage/retrieval services from a database server, and interrupts to a central processor. It also has higher- dimensional applications, such as the spatial distribution of defects on integrated circuit wafers and the volume distribution of contaminants in well water. In such cases, the “events”, which are request arrivals or defects occurrences, are independent. Customers do not conspire to achieve some special pattern in their access to a bank teller; rather they operate as independent agents. The manufacture of hard disks or integrated circuits introduces unavoidable defects because the process pushes the limits of geometric tolerances. Therefore, a perfectly functional process will still occasionally produce a defect, such as a small area on the disk surface where the magnetic material is not spread uniformly or a shorted transistor on an integrated circuit chip. These errors are independent in the sense that a defect at one point does not influence, for better or worse, the chance of a defect at another point. Moreover, if the time interval or spatial area is small, the probability of an event is correspondingly small. This is a characterizing feature of a Poisson distribution: event probability decreases with the window of opportunity and is linear in the limit. A second characterizing feature, negligible probability of two or more events in a small interval, is also present in the mentioned examples. 117
- 118. Poisson Distribution: Formally 118
- 119. Poisson Distribution: Mental Steps This comes from the theory of Generalized Linear Models (GLM). log linear combination of the input features. Hence, the name log-linear model. 119
- 121. Maximizing the Log-Likelihood 121
- 122. Roadmap • Examples of applications of Machine Learning • Encoding objects with features • The Machine Learning framework • Linear models – Perceptron, Winnow , Logistic Regression, Robust Risk Minimization (RRM) • Tree models (Decision Trees DTs) – Classification Decision Trees, Regression Trees • Boosting – AdaBoost • Ranking evaluation – Kendall tau and Spearman’s coefficient • Sequence labeling – Hidden Markov Models (HMMs) 122
- 123. DECISION TREES Why?– DTs are an influential development in ML. Combined in ensemble they provide very competitive performance. We will see ensemble techniques in the next part. 123
- 124. Decision Trees Training instances. Color reflects output variable (classification example). Binary partitioning of the data during training (navigating to leaf node during testing). prediction Training instances are more homogeneous in terms of the output variable (more pure) compared to ancestor nodes. Stopping when instances are homogeneous or 124 small number of instances.
- 125. Decision Tree: Example (classification example with categorical features) Attribute/feature/predicate Parents Visiting Yes No Value of the attribute Cinema Weather Branching factor depends on the number of possible values Sunny Windy Rainy for the attribute (as seen in the training set). Play Stay in tennis Money Rich Poor Predicted classes. Shopping Cinema 125
- 126. Entropy (needed for describing how an attribute is selected.) 1 Example 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 0.2 0.4 0.6 0.8 1 126
- 127. Selecting an Attribute: Information Gain Measure of expected reduction in entropy. instances attribute 127 See Mitchell’97, p.59 for an example.
- 128. Splitting ‘Hairs’ If there are no instances in the current node, inherit statistics (majority class) from parent node. If there are only a small number of instances do not split the node further (statistics are unreliable). If there is more training data, the 128 tree can be “grown” bigger. ?
- 129. ID3 Algorithm 129
- 130. Alternative Attribute Selection: Gain Ratio [Quinlan 1986] instances attribute Examples: all different values. 130
- 131. Alternative Attribute Selection: GINI Index [Corrado Gini: Italian statistician] 131
- 132. Space of Possible Decision Trees Number of possible trees: 132
- 133. Decision Trees and Rule Systems Path from each leaf node to the root represents a conjunctive rule: if (ParentsVisiting==No) & Parents (Weather==Windy) & Visiting (Money==Poor) then Yes No Cinema. Cinema Weather Sunny Windy Rainy Play Stay in tennis Money Rich Poor Shopping Cinema 133
- 134. Decision Trees • Different training sample -> different resulting tree (different structure). • Learning does (conditional) feature selection. 134
- 135. Regression Trees Like classification trees but the prediction is a number (as suggested by “regression”). 1. How do we split? 2. When to stop? predictions (constants) 135
- 136. Regression Trees: How to Split 136
- 137. Regression Trees: Pruning Tree operation where a pre-terminal gets its two leaves collapsed: 137
- 138. Regression Trees: How to Stop 1. Don’t stop. 2. Build big tree. 3. Prune. 4. Evaluate sub-trees. 138
- 139. Roadmap • Examples of applications of Machine Learning • Encoding objects with features • The Machine Learning framework • Linear models – Perceptron, Winnow , Logistic Regression, Robust Risk Minimization (RRM) • Tree models (Decision Trees DTs) – Classification Decision Trees, Regression Trees • Boosting – AdaBoost • Ranking evaluation – Kendall tau and Spearman’s coefficient • Sequence labeling – Hidden Markov Models (HMMs) 139
- 140. BOOSTING 140
- 141. Ensemble Methods object encoded with features classifiers … ENSEMBLE … predictions (response/dependent variable) majority voting/averaging 141
- 142. Where the Systems Come from Sequential ensemble scheme: … 142
- 143. Contrast with Bagging Non-sequential ensemble scheme: DATA Datai are independent of each other (likewise for Sytemi). 143
- 144. Data System Base Procedure: Decision Tree Training instances. Color reflects output variable (classification example). Binary partitioning of the data during training (navigating to leaf node during testing). prediction Training instances are more homogeneous in terms of the output variable (more pure) compared to ancestor nodes. Stopping when instances are homogeneous or 144 small number of instances.
- 145. Ensemble Scheme TRAINING DATA base procedure base procedure Small systems. Original data Don’t need to be perfect. base procedure Weighted data base procedure Weighted data Final prediction (regression) 145
- 146. Ada Boost (classification) Original data Weighted data Weighted data Weighted data normalizing factor. final prediction. 146
- 147. AdaBoost Initializing weights. weight update. normalizing factor. final prediction. 147
- 148. Binary Classifier • Constraint: – Must not have all zero clicks for current week, previous week and week before last [shopping team uses stronger constraint: only instances with non-zero clicks for current week]. • Training: – 1.5M instances. – 0.5M instances (validation). • Feature extraction: – 4.82mins (Cosmos job). • Training time: – 2hrs 20mins. • Testing: – 10k instances: 1sec. 148
- 149. Roadmap • Examples of applications of Machine Learning • Encoding objects with features • The Machine Learning framework • Linear models – Perceptron, Winnow , Logistic Regression, Robust Risk Minimization (RRM) • Tree models (Decision Trees DTs) – Classification Decision Trees, Regression Trees • Boosting – AdaBoost • Ranking evaluation – Kendall tau and Spearman’s coefficient • Sequence labeling – Hidden Markov Models (HMMs) 149
- 150. POPULARITY EVALUATION How do we know we have a good popularity? 150
- 151. Rank Correlation Metrics • • • • • • The two rankings are the same. The two rankings are reverse of each other. Actual input is a set of objects with two rank scores (ties are possible). 151
- 152. Kendall’s Tau Coefficient Considers concordant/discordant pairs in two rankings (each ranking w.r.t. the other): 152
- 153. What is a concordant pair? a a b c c b Need to have the same sign 153
- 154. Kendall Tau: Example A C B D C A D B Pairs: (discordant pairs in red): 154 Observation: Total number of discordant pairs = 2x the discordant pairs in one ranking w.r.t. the other.
- 155. Spearman’s Coefficient Considers ranking differences for the same object: a a b c c b Example: 155
- 156. Rank Intuitions: Setup 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 The sequence <3,1,4,10,5,9,2,6,8,7> is sufficient to encode the two rankings. 156
- 157. Rank Intuitions: Pairs Rankings in complete agreement. Rankings in complete dis-agreement. 157
- 158. Rank Intuitions: Spearman Segment lengths represent R1 rank scores. 158
- 159. Rank Intuitions: Kendall Segment lengths represent R1 rank scores. 159
- 160. What about ties? The position of an object within set of objects with the same scores in the rankings affects the rank correlation. 160 For example, red positioning of oj leads to lower Spearman’s coefficient; green – higher.
- 161. Ties • Kendall: Strict discordance: • Spearman: – Can use per entity upper and lower bounds. – Do as in the Olympics: 161
- 162. Ties: Kendall TauB where: is the number of concordant pairs. is the number of discordant pairs. is the number of objects in the two rankings. http://en.wikipedia.org/wiki/Kendall_tau#Tau-b 162
- 163. Uses of popularity Popularity can be used to augment gain in NDCG by linearly scaling it: 1 3 7 15 31 1 2 3 4 5 poor fair good excellent perfect 163
- 164. Next Steps • How to determine popularity of new entities – Challenge: No historical data. – Usually there is an initial period of high popularity (e.g., a new restaurant is featured in local paper, promotions, etc.). • Good abandonment (no user clicks but good entity in terms of satisfying the user information needs, e.g., phone number). – Use number impressions for named queries. 164
- 165. References 1. Yaser S. Abu-Mostafa, Malik Magdon-Ismail & Hsuan-Tien Lin (2012) Learning From Data. AMLBook. [link] 2. Ethem Alpaydin (2009) Introduction to Machine Learning. 2nd edition. Adaptive Computation and Machine Learning series. MIT Press. [link] 3. David Barber (2012) Bayesian Reasoning and Machine Learning. Cambridge University Press. [link] 4. Ricardo Baeza-Yates & Berthier Ribeiro-Neto (2011) Modern Information Retrieval: The Concepts and Technology behind Search. 2nd Edition. ACM Press Books. [link] 5. Alex Bellos (2010) Alex's Adventures in Numberland. Bloomsbury: New York. [link] 6. Ron Bekkerman, Mikhail Bilenko & John Langford (2011) Scaling up Machine Learning: Parallel and Distributed Approaches. Cambridge University Press. [link] 7. Christopher M. Bishop (2007) Pattern Recognition and Machine Learning. Information Science and Statistics. Springer. [link] 8. George Casella & Roger L. Berger (2001) Statistical Inference. 2nd edition. Duxbury Press. [link] 9. Anirban DasGupta (2011) Probability for Statistics and Machine Learning: Fundamentals and Advanced Topics. Springer Texts in Statistics. Springer. [link] 10. Luc Devroye, László Györfi & Gábor Lugosi (1996) A Probabilistic Theory of Pattern Recognition. Springer. [link] 11. Richard O. Duda, Peter E. Hart & David G. Stork (2000) Pattern Classification. 2nd Edition. Wiley-Interscience. [link] 12. Trevor Hastie, Robert Tibshirani & Jerome Friedman (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd Edition. Springer Series in Statistics. Springer. [link] 13. James L. Johnson (2008) Probability and Statistics for Computer Science. Wiley-Interscience. [link] 14. Daphne Koller & Nir Friedman (2009) Probabilistic Graphical Models: Principles and Techniques. Adaptive Computation and Machine Learning series. MIT Press. [link] 15. David J. C. MacKay (2003) Information Theory, Inference and Learning Algorithms. Cambridge University Press. [link] 16. Zbigniew Michalewicz & David B. Fogel (2004) How to Solve It: Modern Heuristics. 2nd edition. Springer. [link] 17. Tom M. Mitchell (1997) Machine Learning. McGraw-Hill (Science/Engineering/Math). [link] 18. Mehryar Mohri, Afshin Rostamizadeh & Ameet Talwalkar (2012) Foundations of Machine Learning. Adaptive Computation and Machine Learning series. MIT Press. [link] 19. Lior Rokach (2010) Pattern Classification Using Ensemble Methods. World Scientific. [link] 20. Gilbert Strang (1991) Calculus. Wellesley-Cambridge Press. [link] 21. Larry Wasserman (2010) All of Statistics: A Concise Course in Statistical Inference. Springer Texts in Statistics. Springer. [link] 22. Sholom M. Weiss, Nitin Indurkhya & Tong Zhang (2010) Fundamentals of Predictive Text Mining. Texts in Computer Science. Springer. [link] 165
- 166. Roadmap • Examples of applications of Machine Learning • Encoding objects with features • The Machine Learning framework • Linear models – Perceptron, Winnow , Logistic Regression, Robust Risk Minimization (RRM) • Tree models (Decision Trees DTs) – Classification Decision Trees, Regression Trees • Boosting – AdaBoost • Ranking evaluation – Kendall tau and Spearman’s coefficient • Sequence labeling – Hidden Markov Models (HMMs) 166
- 167. SEQUENCE LABELING: HIDDEN MARKOV MODELS (HMMs) 167
- 168. Outline • The guessing game • Tagging preliminaries • Hidden Markov Models • Trellis and the Viterbi algorithm • Implementation (Python) • Complexity of decoding • Parameter estimation and smoothing • Second order models 168
- 169. The Guessing Game • A cow and duck write an email message together. • Goal – figure out which word is written by which animal. 169 The cow/duck illustration of HMMs is due to Ralph Grishman (NYU).
- 170. What’s the Big Deal ? • The vocabularies of the cow and the duck can overlap and it is not clear a priori who wrote a certain word! 170
- 171. The Game (cont) ? ? ? moo hello quack COW ? DUCK moo hello quack 171
- 172. The Game (cont) DUCK COW COW DUCK moo hello quack 172
- 173. What about the Rest of the Animals? ANT ANT ANT ANT ANT COW COW COW COW COW DUCK DUCK DUCK DUCK DUCK PIG PIG PIG PIG PIG ZEBRA ZEBRA ZEBRA ZEBRA ZEBRA word1 word2 word3 word4 word5 173
- 174. A Game for Adults • Instead of guessing which animal is associated with each word guess the corresponding POS tag of a word. Pierre/NNP Vinken/NNP ,/, 61/CD years/NNS old/JJ ,/, will/MD join/VB the/DT board/NN as/IN a/DT nonexecutive/JJ director/NN Nov./NNP 29/CD ./. 174
- 175. POS Tags "CC", "CD", "DT", "EX", "FW", "IN", "JJ", "JJR", "JJS", "LS", "MD","NN", "NNS","NNP", "NNPS", "PDT", "POS", "PRP", "PRP$", "RB", "RBR", "RBS", "RP", "SYM", "TO", "UH", "VB", "VBD", "VBG", "VBN", "VBP", "VBZ", "WDT", "WP", "WP$", "WRB", "#", "$", ".",",", ":", "(", ")", "`", "``", "'", "''" 175
- 176. Tagging Preliminaries • We want the best set of tags for a sequence of words (a sentence) • W — a sequence of words • T — a sequence of tags ^ T arg max P(T | W ) T 176
- 177. Bayes’ Theorem (1763) likelihood prior posterior P(W | T ) P(T ) P(T | W ) P(W ) marginal likelihood Reverend Thomas Bayes — Presbyterian minister (1702-1761) 177
- 178. Applying Bayes’ Theorem • How do we approach P(T|W) ? • Use Bayes’ theorem P(W | T ) P(T ) arg max P(T | W ) arg max T T P(W ) • So what? Why is it better? • Ignore the denominator (and the question): P(W | T ) P(T ) arg max P(T | W ) arg max arg max P(W | T ) P(T ) T T P(W ) T 178
- 179. Tag Sequence Probability How do we get the probability P(T) of a specific tag sequence T? • Count the number of times a sequence occurs and divide by the number of sequences of that length — not likely! – Use chain rule 179
- 180. Chain Rule history P (T ) P (t1 ,..., t n ) P (t1 ) P (t 2 | t1 ) P (t3 | t1t 2 ) ... P (t n | t1 ,..., t n 1 ) P(T) is a product of the probability of the N-grams that make it up Make a Markov assumption: the current tag depends on the previous one only: n P(t1 ,..., tn ) P(t1 ) P(ti | ti 1 ) i 2 180
- 181. Transition Probabilities • Use counts from a large hand-tagged corpus. • For bi-grams, count all the ti–1 ti pairs c(ti 1ti ) P(ti | ti 1 ) c(ti 1 ) • Some counts are zero – we’ll use smoothing to address this issue later. 181
- 182. What about P(W|T) ? • First it's odd—it is asking the probability of seeing “The white horse” given “Det Adj Noun”! – Collect up all the times you see that tag sequence and see how often “The white horse” shows up … • Assume each word in the sequence depends only on its corresponding tag: n P(W | T ) P( wi | ti ) i 1 emission probabilities 182
- 183. Emission Probabilities • What proportion of times is the word wi associated with the tag ti (as opposed to another word): c( wi , ti ) P( wi | ti ) c(ti ) 183
- 184. The “Standard” Model arg max P (T | W ) T P (W | T ) P (T ) arg max T P (W ) arg max P (W | T ) P (T ) T n arg max P ( wi | ti ) P (ti | ti 1 ) T i 1 184
- 185. Hidden Markov Models • Stochastic process: A sequence 1 , 2,… of random variables based on the same sample space . • Probabilities for the first observation: P( 1 x j ) for each outcome x j • Next step given previous history: P( t 1 xit 1 | 1 xi1 , ... , t xit ) 185
- 186. Markov Chain • A Markov Chain is a stochastic process with the Markov property: P( t 1 xit 1 | 1 xi1 , ... , t xit ) P( t 1 xit 1 | t xit ) • Outcomes are called states. • Probabilities for next step – weighted finite state automata. 186
- 187. State Transitions w/ Probabilities 0.5 COW 0.2 1.0 END START 0.3 0.3 0.2 DUCK 0.5 187
- 188. Markov Model Markov chain 0.5 moo:0.9 where each state hello:0.1 can output signals COW ^:1.0 0.2 $:1.0 1.0 END START 0.3 0.3 0.2 DUCK (like “Moore machines”): hello:0.4 quack:0.6 0.5 188
- 189. The Issue Was • A given output symbol can potentially be emitted by more than one state — omnipresent ambiguity in natural language. 189
- 190. Markov Model Finite set of states: {s1,..., sm} Signal alphabet: { 1,..., k } Transition matrix: P [ pij ] where pij P( t 1 sj | t si ) Emission probabilities: A [aij ] where aij P( t j | t si ) Initial probability vector: v [v1 ,..., vm ] where v j P( 1 sj) 190
- 191. Graphical Model STATE TAG … OUTPUT word 191
- 192. Hidden Markov Model • A Markov Model for which it is not possible to observe the sequence of states. • S: unknown — sequence of states S * • O: known — sequence of observations O * arg max P( S | O) S tags words 192
- 193. The State Space moo:0.9 hello:0.1 0.5 0.5 COW COW COW 1.0 0.2 0.3 0.3 START END 0.3 0.3 0.0 0.2 0.5 0.5 DUCK DUCK DUCK hello:0.4 quack:0.6 moo hello quack More on how the probabilities come about (training) later. 193
- 194. Optimal State Sequence: The Viterbi Algorithm We define the joint probability of the most likely sequence from time 1 to time t ending in state si and the observed sequence O≤t up to time t: t (i) max P(S t 1 , t si ; O t ) S t 1 max P( 1 si1 ,..., t 1 sit 1 , t si ; O t ) si1 ,..., sit 1 194
- 195. Key Observation The most likely partial derivation leading to state si at position t consists of: – the most likely partial derivation leading to some state sit-1 at the previous position t-1, – followed by the transition from sit-1 to si. 195
- 196. Viterbi (cont) Note: 1 (i ) vi aik 1 where vi P( 1 si ) and aik 1 P( t k1 | t si ) We will show that: t ( j ) [max t 1 (i) pij ] a jk t i 196
- 197. Recurrence Equation t ( j) max P ( S t 1 , t s j ;O t ) S t 1 max max P ( S t 2 , t 1 si , t s j ;O t 1 , t kt ) i S t 2 max max P ( t sj; t kt |S t 2 , t 1 si ; O t 1 ) i S t 2 k1 P( S t 2 , t 1 si ; O t 1 ) max max P ( t sj | t 1 si ) P ( t kt | t sj) i S t 2 P( S t 2 , t 1 si ; O t 1 ) [max P ( t sj | t 1 si ) max P ( S t 2 , t 1 si ; O t )] 1 i S t 2 P( t kt | t sj) [max pij t 1 (i )] a jk t i 197
- 198. Back Pointers • The predecessor of state si in the path corresponding to t(i) : t ( j ) argmax ( t 1 (i ) pij ) 1 i m • Optimal state sequence: * s kT argmax n (i ) 1 i m * * skt t 1 ( skt 1 ) for t 1, ... , n 1 198
- 199. The Trellis moo hello quack $ t=0 t=1 t=2 t=3 t=4 START 1 0 0 0 0 COW 0 0.9 0.045 0 0 0.0081 0 DUCK 0 0 0.108 0.0324 END 0 0 0 0 0.00648 199
- 200. Implementation (Python) observations = ['^','moo','hello','quack','$'] # signal sequence states = ['start','cow','duck','end'] # Transition probabilities - p[FromState][ToState] = probability p = {'start': {'cow':1.0}, 'cow': {'cow' :0.5, 'duck':0.3, 'end' :0.2}, 'duck': {'duck':0.5, 'cow' :0.3, 'end' :0.2}} # Emission probabilities; special emission symbol '$' for 'end' state a = {'cow' : {'moo' :0.9,'hello':0.1, 'quack':0.0, '$':0.0}, 'duck': {'moo' :0.0,'hello':0.4, 'quack':0.6, '$':0.0}, 'end' : {'moo' :0.0,'hello':0.0, 'quack':0.0, '$':1.0}} 200

- Outcomes:POSITIVENEGATIVEMIXEDNEUTRALUNKNOWN
- Polonius, Hamlet Act 2, scene 2, 193–206
- Or the Density(entity) can be normalized click through rate for entities near the given input entity.
- N parts whose order is then reversed:Spearman 2 parts : -0.500416782439567Spearman 3 parts: -0.778271742Spearman 4 parts: -0.875520978049458150597Spearman 5 parts: -0.920533481522645Spearman 6 parts: -0.944984717977216 Spearman 10 parts: -0.980550152820228Spearman 15 parts: -0.991664351208669
- N parts whose order is then reversed:Kendall 2 parts: -0.0169491525423728Kendall 3 parts: -0.35593220338983Kendall 4 parts: -0.525423728813559Kendall 5 parts: -0.627118644067797Kendall 6 parts: -0.694915254237288Kendall 10 parts: -0.830508474576271Kendall 15 parts: -0.898305084745763
- Josiah Godfrey (for data).Patrick Haluptzok on matching.