Introduction to Machine Learning
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Here are the key calculations: 1) Probability that persons p and q will be at the same hotel on a given day d is 1/100 × 1/100 × 10-5 = 10-9, since there are 100 hotels and each person stays in a hotel with probability 10-5 on any given day. 2) Probability that p and q will be at the same hotel on given days d1 and d2 is (10-9) × (10-9) = 10-18, since the events are independent.
Introduction to Machine Learning
- 1. Introduction to Machine Learning Lior Rokach Department of Information Systems Engineering Ben-Gurion University of the Negev
- 2. About Me Prof. Lior Rokach Department of Information Systems Engineering Faculty of Engineering Sciences Head of the Machine Learning Lab Ben-Gurion University of the Negev Email: [email protected] http://www.ise.bgu.ac.il/faculty/liorr/ PhD (2004) from Tel Aviv University
- 3. Machine Learning • Herbert Alexander Simon: “Learning is any process by which a system improves performance from experience.” • “Machine Learning is concerned with computer programs that automatically improve their performance through Herbert Simon experience. “ Turing Award 1975 Nobel Prize in Economics 1978
- 4. Why Machine Learning? • Develop systems that can automatically adapt and customize themselves to individual users. – Personalized news or mail filter • Discover new knowledge from large databases (data mining). – Market basket analysis (e.g. diapers and beer) • Ability to mimic human and replace certain monotonous tasks - which require some intelligence. • like recognizing handwritten characters • Develop systems that are too difficult/expensive to construct manually because they require specific detailed skills or knowledge tuned to a specific task (knowledge engineering bottleneck). 4
- 5. Why now? • Flood of available data (especially with the advent of the Internet) • Increasing computational power • Growing progress in available algorithms and theory developed by researchers • Increasing support from industries 5
- 7. The concept of learning in a ML system • Learning = Improving with experience at some task – Improve over task T, – With respect to performance measure, P – Based on experience, E. 7
- 8. Motivating Example Learning to Filter Spam Example: Spam Filtering Spam - is all email the user does not want to receive and has not asked to receive T: Identify Spam Emails P: % of spam emails that were filtered % of ham/ (non-spam) emails that were incorrectly filtered-out E: a database of emails that were labelled by users
- 10. The Learning Process Model Learning Model Testing
- 11. The Learning Process in our Example Model Learning Model Testing ● Number of recipients ● Size of message ● Number of attachments ● Number of "re's" in the subject line Email Server …
- 12. Data Set Target Input Attributes Attribute Number of Email Country Customer Email Type new Length (K) (IP) Type Recipients 0 2 Germany Gold Ham 1 4 Germany Silver Ham 5 2 Nigeria Bronze Spam Instances 2 4 Russia Bronze Spam 3 4 Germany Bronze Ham 0 1 USA Silver Ham 4 2 USA Silver Spam Numeric Nominal Ordinal
- 13. Step 4: Model Learning Learner Classifier Database Inducer Classification Model Training Set Induction Algorithm
- 14. Step 5: Model Testing Database Learner Training Set Inducer Classifier Induction Algorithm Classification Model
- 18. Error
- 22. Linear Classifiers Email Length How would you classify this data? New Recipients
- 23. Linear Classifiers Email Length How would you classify this data? New Recipients
- 24. When a new email is sent 1. We first place the new email in the space 2. Classify it according to the subspace in which it resides Email Length New Recipients
- 25. Linear Classifiers Email Length How would you classify this data? New Recipients
- 26. Linear Classifiers Email Length How would you classify this data? New Recipients
- 27. Linear Classifiers Email Length How would you classify this data? New Recipients
- 28. Linear Classifiers Email Length Any of these would be fine.. ..but which is best? New Recipients
- 29. Classifier Margin Define the margin of a linear classifier as Email Length the width that the boundary could be increased by before hitting a datapoint. New Recipients
- 30. Maximum Margin The maximum margin linear Email Length classifier is the linear classifier with the, maximum margin. This is the simplest kind of SVM (Called an LSVM) New Recipients Linear SVM
- 31. No Linear Classifier can cover all instances Email Length How would you classify this data? New Recipients
- 32. • Ideally, the best decision boundary should be the one which provides an optimal performance such as in the following figure
- 33. No Linear Classifier can cover all instances Email Length New Recipients
- 34. • However, our satisfaction is premature because the central aim of designing a classifier is to correctly classify novel input Issue of generalization!
- 35. Which one? 2 Errors 0 Errors Simple model Complicated model
- 36. Evaluating What’s Been Learned 1. We randomly select a portion of the data to be used for training (the training set) 2. Train the model on the training set. 3. Once the model is trained, we run the model on the remaining instances (the test set) to see how it performs Confusion Matrix Classified As Email Length Blue Red Actual Blue 7 1 Red 0 5 New Recipients
- 37. The Non-linearly separable case Email Length New Recipients
- 38. The Non-linearly separable case Email Length New Recipients
- 39. The Non-linearly separable case Email Length New Recipients
- 40. The Non-linearly separable case Email Length New Recipients
- 41. Lazy Learners • Generalization beyond the training data is delayed until a new instance is provided to the system Training Set Learner Classifier
- 42. Lazy Learners Instance-based learning Training Set
- 43. Lazy Learner: k-Nearest Neighbors • What should be k? • Which distance measure should be used? K=3 Email Length New Recipients
- 44. Decision tree – A flow-chart-like tree structure – Internal node denotes a test on an attribute – Branch represents an outcome of the test – Leaf nodes represent class labels or class distribution
- 45. Top Down Induction of Decision Trees Email Len <1.8 ≥1.8 Email Length Spam Ham 1 Error 8 Errors New Recipients A single level decision tree is also known as Decision Stump
- 46. Top Down Induction of Decision Trees Email Len <1.8 ≥1.8 Email Length Spam Ham 1 Error 8 Errors New Recip New Recipients <2.3 ≥2.3 Spam Ham 4 Errors 11 Errors
- 47. Top Down Induction of Decision Trees Email Len <1.8 ≥1.8 Email Length Spam Ham 1 Error 8 Errors New Recip New Recipients <2.3 ≥2.3 Spam Ham 4 Errors 11 Errors
- 48. Top Down Induction of Decision Trees Email Len <1.8 ≥1.8 Email Length Spam Email Len 1 Error <4 ≥4 Ham Spam New Recipients 3 Errors 1 Error
- 49. Top Down Induction of Decision Trees Email Len <1.8 ≥1.8 Email Length Spam Email Len 1 Error <4 ≥4 Spam New Recip New Recipients 1 Error <1 ≥1 Spam Ham 0 Errors 1 Error
- 50. Which One?
- 51. Overfitting and underfitting Error Tree Size Overtraining: means that it learns the training set too well – it overfits to the training set such that it performs poorly on the test set. Underfitting: when model is too simple, both training and test errors are large
- 52. Neural Network Model Inputs Output Age 34 .6 . .2 S 4 0.6 .1 .5 Gender 2 .3 .2 S S .8 .7 “Probability of 4 beingAlive” Stage .2 Dependent Independent Weights HiddenLaye Weights variable variables r Prediction Machine Learning, Dr. Lior Rokach, Ben-Gurion University
- 53. “Combined logistic models” Inputs Output Age 34 .6 .5 0.6 .1 Gender 2 S .7 .8 “Probability of 4 beingAlive” Stage Dependent Independent Weights HiddenLaye Weights variable variables r Prediction Machine Learning, Dr. Lior Rokach, Ben-Gurion University
- 54. Inputs Output Age 34 .2 .5 0.6 Gender 2 .3 S “Probability of .8 4 beingAlive” Stage .2 Dependent Independent Weights HiddenLaye Weights variable variables r Prediction Machine Learning, Dr. Lior Rokach, Ben-Gurion University
- 55. Inputs Output Age 34 .6 .2 .5 0.6 .1 Gender 1 .3 S .7 “Probability of .8 4 beingAlive” Stage .2 Dependent Independent Weights HiddenLaye Weights variable variables r Prediction Machine Learning, Dr. Lior Rokach, Ben-Gurion University
- 56. Age 34 .6 . .2 S 4 0.6 .1 .5 Gender 2 .3 .2 S S .8 .7 “Probability of 4 beingAlive” Stage .2 Dependent Independent Weights HiddenLaye Weights variable variables r Prediction Machine Learning, Dr. Lior Rokach, Ben-Gurion University
- 57. Ensemble Learning • The idea is to use multiple models to obtain better predictive performance than could be obtained from any of the constituent models. • Boosting involves incrementally building an ensemble by training each new model instance to emphasize the training instances that previous models misclassified.
- 58. Example of Ensemble of Weak Classifiers Training Combined classifier
- 59. Main Principles
- 60. Occam's razor (14th-century) • In Latin “lex parsimoniae”, translating to “law of parsimony” • The explanation of any phenomenon should make as few assumptions as possible, eliminating those that make no difference in the observable predictions of the explanatory hypothesis or theory • The Occam Dilemma: Unfortunately, in ML, accuracy and simplicity (interpretability) are in conflict.
- 61. No Free Lunch Theorem in Machine Learning (Wolpert, 2001) • “For any two learning algorithms, there are just as many situations (appropriately weighted) in which algorithm one is superior to algorithm two as vice versa, according to any of the measures of "superiority"
- 62. So why developing new algorithms? • Practitioner are mostly concerned with choosing the most appropriate algorithm for the problem at hand • This requires some a priori knowledge – data distribution, prior probabilities, complexity of the problem, the physics of the underlying phenomenon, etc. • The No Free Lunch theorem tells us that – unless we have some a priori knowledge – simple classifiers (or complex ones for that matter) are not necessarily better than others. However, given some a priori information, certain classifiers may better MATCH the characteristics of certain type of problems. • The main challenge of the practitioner is then, to identify the correct match between the problem and the classifier! …which is yet another reason to arm yourself with a diverse set of learner arsenal !
- 63. Less is More The Curse of Dimensionality (Bellman, 1961)
- 64. Less is More The Curse of Dimensionality • Learning from a high-dimensional feature space requires an enormous amount of training to ensure that there are several samples with each combination of values. • With a fixed number of training instances, the predictive power reduces as the dimensionality increases. • As a counter-measure, many dimensionality reduction techniques have been proposed, and it has been shown that when done properly, the properties or structures of the objects can be well preserved even in the lower dimensions. • Nevertheless, naively applying dimensionality reduction can lead to pathological results.
- 65. While dimensionality reduction is an important tool in machine learning/data mining, we must always be aware that it can distort the data in misleading ways. Above is a two dimensional projection of an intrinsically three dimensional world….
- 66. Original photographer unknown See also www.cs.gmu.edu/~jessica/DimReducDanger.htm (c) eamonn keogh
- 67. Screen dumps of a short video from www.cs.gmu.edu/~jessica/DimReducDanger.htm I recommend you imbed the original video instead A cloud of points in 3D Can be projected into 2D XY or XZ or YZ In 2D XZ we see a triangle In 2D YZ we see a square In 2D XY we see a circle
- 68. Less is More? • In the past the published advice was that high dimensionality is dangerous. • But, Reducing dimensionality reduces the amount of information available for prediction. • Today: try going in the opposite direction: Instead of reducing dimensionality, increase it by adding many functions of the predictor variables. • The higher the dimensionality of the set of features, the more likely it is that separation occurs.
- 69. Meaningfulness of Answers • A big data-mining risk is that you will “discover” patterns that are meaningless. • Statisticians call it Bonferroni’s principle: (roughly) if you look in more places for interesting patterns than your amount of data will support, you are bound to find crap. 69
- 70. Examples of Bonferroni’s Principle 1. Track Terrorists 2. The Rhine Paradox: a great example of how not to conduct scientific research. 70
- 71. Why Tracking Terrorists Is (Almost) Impossible! • Suppose we believe that certain groups of evil- doers are meeting occasionally in hotels to plot doing evil. • We want to find (unrelated) people who at least twice have stayed at the same hotel on the same day. 71
- 72. The Details • 109 people being tracked. • 1000 days. • Each person stays in a hotel 1% of the time (10 days out of 1000). • Hotels hold 100 people (so 105 hotels). • If everyone behaves randomly (I.e., no evil- doers) will the data mining detect anything suspicious? 72
- 73. p at q at some Calculations – (1) some hotel Same hotel hotel • Probability that given persons p and q will be at the same hotel on given day d : – 1/100 1/100 10-5 = 10-9. • Probability that p and q will be at the same hotel on given days d1 and d2: – 10-9 10-9 = 10-18. • Pairs of days: – 5105. 73
- 74. Calculations – (2) • Probability that p and q will be at the same hotel on some two days: – 5105 10-18 = 510-13. • Pairs of people: – 51017. • Expected number of “suspicious” pairs of people: – 51017 510-13 = 250,000. 74
- 75. Conclusion • Suppose there are (say) 10 pairs of evil-doers who definitely stayed at the same hotel twice. • Analysts have to sift through 250,010 candidates to find the 10 real cases. – Not gonna happen. – But how can we improve the scheme? 75
- 76. Moral • When looking for a property (e.g., “two people stayed at the same hotel twice”), make sure that the property does not allow so many possibilities that random data will surely produce facts “of interest.” 76
- 77. Rhine Paradox – (1) • Joseph Rhine was a parapsychologist in the 1950’s who hypothesized that some people had Extra-Sensory Perception. • He devised (something like) an experiment where subjects were asked to guess 10 hidden cards – red or blue. • He discovered that almost 1 in 1000 had ESP – they were able to get all 10 right! 77
- 78. Rhine Paradox – (2) • He told these people they had ESP and called them in for another test of the same type. • Alas, he discovered that almost all of them had lost their ESP. • What did he conclude? – Answer on next slide. 78
- 79. Rhine Paradox – (3) • He concluded that you shouldn’t tell people they have ESP; it causes them to lose it. 79
- 80. Moral • Understanding Bonferroni’s Principle will help you look a little less stupid than a parapsychologist. 80
- 81. Instability and the Rashomon Effect • Rashomon is a Japanese movie in which four people, from different vantage points, witness a criminal incident. When they come to testify in court, they all report the same facts, but their stories of what happened are very different. • The Rashomon effect is the effect of the subjectivity of perception on recollection. • The Rashomon Effect in ML is that there is often a multitude of classifiers of giving about the same minimum error rate. • For example in decition trees, if the training set is perturbed only slightly, I can get a tree quite different from the original but with almost the same test set error.
- 82. The Wisdom of Crowds Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations • Under certain controlled conditions, the aggregation of information in groups, resulting in decisions that are often superior to those that can been made by any single - even experts. • Imitates our second nature to seek several opinions before making any crucial decision. We weigh the individual opinions, and combine them to reach a final decision
- 83. Committees of Experts – “ … a medical school that has the objective that all students, given a problem, come up with an identical solution” • There is not much point in setting up a committee of experts from such a group - such a committee will not improve on the judgment of an individual. • Consider: – There needs to be disagreement for the committee to have the potential to be better than an individual.
- 85. Supervised Learning - Multi Class Email Length New Recipients
- 86. Supervised Learning - Multi Label Multi-label learning refers to the classification problem where each example can be assigned to multiple class labels simultaneously
- 87. Supervised Learning - Regression Find a relationship between a numeric dependent variable and one or more independent variables Email Length New Recipients
- 88. Unsupervised Learning - Clustering Clustering is the assignment of a set of observations into subsets (called clusters) so that observations in the same cluster are similar in some sense Email Length New Recipients
- 89. Unsupervised Learning–Anomaly Detection Detecting patterns in a given data set that do not conform to an established normal behavior. Email Length New Recipients
- 90. Source of Training Data • Provided random examples outside of the learner’s control. – Passive Learning – Negative examples available or only positive? Semi-Supervised Learning – Imbalanced • Good training examples selected by a “benevolent teacher.” – “Near miss” examples • Learner can query an oracle about class of an unlabeled example in the environment. – Active Learning • Learner can construct an arbitrary example and query an oracle for its label. • Learner can run directly in the environment without any human guidance and obtain feedback. – Reinforcement Learning • There is no existing class concept – A form of discovery – Unsupervised Learning • Clustering • Association Rules • 90
- 91. Other Learning Tasks • Other Supervised Learning Settings – Multi-Class Classification – Multi-Label Classification – Semi-supervised classification – make use of labeled and unlabeled data – One Class Classification – only instances from one label are given • Ranking and Preference Learning • Sequence labeling • Cost-sensitive Learning • Online learning and Incremental Learning- Learns one instance at a time. • Concept Drift • Multi-Task and Transfer Learning • Collective classification – When instances are dependent!
- 92. Software
- 93. Want to Learn More?
- 94. Want to Learn More? • Thomas Mitchell (1997), Machine Learning, Mcgraw-Hill. • R. Duda, P. Hart, and D. Stork (2000), Pattern Classification, Second Edition. • Ian H. Witten, Eibe Frank, Mark A. Hall (2011), Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, The Morgan Kaufmann • Oded Maimon, Lior Rokach (2010), Data Mining and Knowledge Discovery Handbook, Second Edition, Springer.