# Data Mining Lecture_10(b).pptx

Subrata Kumer PaulAssistant Professor, Dept. of CSE, BAUET at Subrata Kumer Paul
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### Data Mining Lecture_10(b).pptx

• 1. DATA MINING LECTURE 10B Classification k-nearest neighbor classifier Naïve Bayes Logistic Regression Support Vector Machines Subrata Kumer Paul Assistant Professor, Dept. of CSE, BAUET sksubrata96@gmail.com
• 3. Illustrating Classification Task Apply Model Induction Deduction Learn Model Model Tid Attrib1 Attrib2 Attrib3 Class 1 Yes Large 125K No 2 No Medium 100K No 3 No Small 70K No 4 Yes Medium 120K No 5 No Large 95K Yes 6 No Medium 60K No 7 Yes Large 220K No 8 No Small 85K Yes 9 No Medium 75K No 10 No Small 90K Yes 10 Tid Attrib1 Attrib2 Attrib3 Class 11 No Small 55K ? 12 Yes Medium 80K ? 13 Yes Large 110K ? 14 No Small 95K ? 15 No Large 67K ? 10 Test Set Learning algorithm Training Set
• 4. Instance-Based Classifiers Atr1 ……... AtrN Class A B B C A C B Set of Stored Cases Atr1 ……... AtrN Unseen Case • Store the training records • Use training records to predict the class label of unseen cases
• 5. Instance Based Classifiers • Examples: • Rote-learner • Memorizes entire training data and performs classification only if attributes of record match one of the training examples exactly • Nearest neighbor classifier • Uses k “closest” points (nearest neighbors) for performing classification
• 6. Nearest Neighbor Classifiers • Basic idea: • “If it walks like a duck, quacks like a duck, then it’s probably a duck” Training Records Test Record Compute Distance Choose k of the “nearest” records
• 7. Nearest-Neighbor Classifiers  Requires three things – The set of stored records – Distance Metric to compute distance between records – The value of k, the number of nearest neighbors to retrieve  To classify an unknown record: 1. Compute distance to other training records 2. Identify k nearest neighbors 3. Use class labels of nearest neighbors to determine the class label of unknown record (e.g., by taking majority vote) Unknown record
• 8. Definition of Nearest Neighbor X X X (a) 1-nearest neighbor (b) 2-nearest neighbor (c) 3-nearest neighbor K-nearest neighbors of a record x are data points that have the k smallest distance to x
• 9. 1 nearest-neighbor Voronoi Diagram defines the classification boundary The area takes the class of the green point
• 10. Nearest Neighbor Classification • Compute distance between two points: • Euclidean distance • Determine the class from nearest neighbor list • take the majority vote of class labels among the k- nearest neighbors • Weigh the vote according to distance • weight factor, w = 1/d2    i i i q p q p d 2 ) ( ) , (
• 11. Nearest Neighbor Classification… • Choosing the value of k: • If k is too small, sensitive to noise points • If k is too large, neighborhood may include points from other classes X
• 12. Nearest Neighbor Classification… • Scaling issues • Attributes may have to be scaled to prevent distance measures from being dominated by one of the attributes • Example: • height of a person may vary from 1.5m to 1.8m • weight of a person may vary from 90lb to 300lb • income of a person may vary from \$10K to \$1M
• 13. Nearest Neighbor Classification… • Problem with Euclidean measure: • High dimensional data • curse of dimensionality • Can produce counter-intuitive results 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 vs d = 1.4142 d = 1.4142  Solution: Normalize the vectors to unit length
• 14. Nearest neighbor Classification… • k-NN classifiers are lazy learners • It does not build models explicitly • Unlike eager learners such as decision trees • Classifying unknown records are relatively expensive • Naïve algorithm: O(n) • Need for structures to retrieve nearest neighbors fast. • The Nearest Neighbor Search problem.
• 15. Nearest Neighbor Search • Two-dimensional kd-trees • A data structure for answering nearest neighbor queries in R2 • kd-tree construction algorithm • Select the x or y dimension (alternating between the two) • Partition the space into two with a line passing from the median point • Repeat recursively in the two partitions as long as there are enough points
• 22. region(u) – all the black points in the subtree of u 2-dimensional kd-trees Nearest Neighbor Search
• 23.  A binary tree:  Size O(n)  Depth O(logn)  Construction time O(nlogn)  Query time: worst case O(n), but for many cases O(logn) Generalizes to d dimensions  Example of Binary Space Partitioning 2-dimensional kd-trees Nearest Neighbor Search
• 25. Support Vector Machines • Find a linear hyperplane (decision boundary) that will separate the data
• 26. Support Vector Machines • One Possible Solution B1
• 27. Support Vector Machines • Another possible solution B2
• 28. Support Vector Machines • Other possible solutions B2
• 29. Support Vector Machines • Which one is better? B1 or B2? • How do you define better? B1 B2
• 30. Support Vector Machines • Find hyperplane maximizes the margin => B1 is better than B2 B1 B2 b11 b12 b21 b22 margin
• 31. Support Vector Machines B1 b11 b12 0    b x w   1     b x w   1     b x w               1 b x w if 1 1 b x w if 1 ) (      x f || || 2 Margin w  
• 32. Support Vector Machines • We want to maximize: • Which is equivalent to minimizing: • But subjected to the following constraints: • This is a constrained optimization problem • Numerical approaches to solve it (e.g., quadratic programming) 2 || || 2 Margin w   2 || || ) ( 2 w w L   𝑤 ∙ 𝑥𝑖 + 𝑏 ≥ 1 if 𝑦𝑖 = 1 𝑤 ∙ 𝑥𝑖 + 𝑏 ≤ −1 if 𝑦𝑖 = −1
• 33. Support Vector Machines • What if the problem is not linearly separable?
• 34. Support Vector Machines • What if the problem is not linearly separable? 𝜉𝑖 𝑤
• 35. Support Vector Machines • What if the problem is not linearly separable? • Introduce slack variables • Need to minimize: • Subject to:           N i k i C w w L 1 2 2 || || ) (   𝑤 ∙ 𝑥𝑖 + 𝑏 ≥ 1 − 𝜉𝑖 if 𝑦𝑖 = 1 𝑤 ∙ 𝑥𝑖 + 𝑏 ≤ −1 + 𝜉𝑖if 𝑦𝑖 = −1
• 36. Nonlinear Support Vector Machines • What if decision boundary is not linear?
• 37. Nonlinear Support Vector Machines • Transform data into higher dimensional space Use the Kernel Trick
• 39. Classification via regression • Instead of predicting the class of an record we want to predict the probability of the class given the record • The problem of predicting continuous values is called regression problem • General approach: find a continuous function that models the continuous points.
• 40. Example: Linear regression • Given a dataset of the form (𝑥1, 𝑦1) , … , (𝑥𝑛, 𝑦𝑛) find a linear function that given the vector 𝑥𝑖 predicts the 𝑦𝑖 value as 𝑦𝑖 ′ = 𝑤𝑇𝑥𝑖 • Find a vector of weights 𝑤 that minimizes the sum of square errors 𝑖 𝑦𝑖 ′ − 𝑦𝑖 2 • Several techniques for solving the problem.
• 41. Classification via regression • Assume a linear classification boundary 𝑤 ⋅ 𝑥 = 0 𝑤 ⋅ 𝑥 > 0 𝑤 ⋅ 𝑥 < 0 For the positive class the bigger the value of 𝑤 ⋅ 𝑥, the further the point is from the classification boundary, the higher our certainty for the membership to the positive class • Define 𝑃(𝐶+|𝑥) as an increasing function of 𝑤 ⋅ 𝑥 For the negative class the smaller the value of 𝑤 ⋅ 𝑥, the further the point is from the classification boundary, the higher our certainty for the membership to the negative class • Define 𝑃(𝐶−|𝑥) as a decreasing function of 𝑤 ⋅ 𝑥
• 42. Logistic Regression 𝑓 𝑡 = 1 1 + 𝑒−𝑡 𝑃 𝐶+ 𝑥 = 1 1 + 𝑒−𝑤⋅𝑥 𝑃 𝐶− 𝑥 = 𝑒−𝑤⋅𝑥 1 + 𝑒−𝑤⋅𝑥 log 𝑃 𝐶+ 𝑥 𝑃 𝐶− 𝑥 = 𝑤 ⋅ 𝑥 Logistic Regression: Find the vector 𝑤 that maximizes the probability of the observed data The logistic function Linear regression on the log-odds ratio
• 43. Logistic Regression • Produces a probability estimate for the class membership which is often very useful. • The weights can be useful for understanding the feature importance. • Works for relatively large datasets • Fast to apply.
• 45. Bayes Classifier • A probabilistic framework for solving classification problems • A, C random variables • Joint probability: Pr(A=a,C=c) • Conditional probability: Pr(C=c | A=a) • Relationship between joint and conditional probability distributions • Bayes Theorem: ) ( ) ( ) | ( ) | ( A P C P C A P A C P  ) Pr( ) | Pr( ) Pr( ) | Pr( ) , Pr( C C A A A C A C    
• 46. Bayesian Classifiers • Consider each attribute and class label as random variables Tid Refund Marital Status Taxable Income Evade 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No 4 Yes Married 120K No 5 No Divorced 95K Yes 6 No Married 60K No 7 Yes Divorced 220K No 8 No Single 85K Yes 9 No Married 75K No 10 No Single 90K Yes 10 categorical categorical continuous class Evade C Event space: {Yes, No} P(C) = (0.3, 0.7) Refund A1 Event space: {Yes, No} P(A1) = (0.3,0.7) Martial Status A2 Event space: {Single, Married, Divorced} P(A2) = (0.4,0.4,0.2) Taxable Income A3 Event space: R P(A3) ~ Normal(,)
• 47. Bayesian Classifiers • Given a record X over attributes (A1, A2,…,An) • E.g., X = (‘Yes’, ‘Single’, 125K) • The goal is to predict class C • Specifically, we want to find the value c of C that maximizes P(C=c| X) • Maximum Aposteriori Probability estimate • Can we estimate P(C| X) directly from data? • This means that we estimate the probability for all possible values of the class variable.
• 48. Bayesian Classifiers • Approach: • compute the posterior probability P(C | A1, A2, …, An) for all values of C using the Bayes theorem • Choose value of C that maximizes P(C | A1, A2, …, An) • Equivalent to choosing value of C that maximizes P(A1, A2, …, An|C) P(C) • How to estimate P(A1, A2, …, An | C )? ) ( ) ( ) | ( ) | ( 2 1 2 1 2 1 n n n A A A P C P C A A A P A A A C P    
• 49. Naïve Bayes Classifier • Assume independence among attributes Ai when class is given: • 𝑃(𝐴1, 𝐴2, … , 𝐴𝑛|𝐶) = 𝑃(𝐴1|𝐶) 𝑃(𝐴2 𝐶 ⋯ 𝑃(𝐴𝑛|𝐶) • We can estimate P(Ai| C) for all values of Ai and C. • New point X is classified to class c if 𝑃 𝐶 = 𝑐 𝑋 = 𝑃 𝐶 = 𝑐 𝑖 𝑃(𝐴𝑖|𝑐) is maximum over all possible values of C.
• 50. How to Estimate Probabilities from Data? • Class Prior Probability: 𝑃 𝐶 = 𝑐 = 𝑁𝑐 𝑁 e.g., P(C = No) = 7/10, P(C = Yes) = 3/10 • For discrete attributes: 𝑃 𝐴𝑖 = 𝑎 𝐶 = 𝑐 = 𝑁𝑎,𝑐 𝑁𝑐 where 𝑁𝑎,𝑐 is number of instances having attribute 𝐴𝑖 = 𝑎 and belongs to class 𝑐 • Examples: P(Status=Married|No) = 4/7 P(Refund=Yes|Yes)=0 Tid Refund Marital Status Taxable Income Evade 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No 4 Yes Married 120K No 5 No Divorced 95K Yes 6 No Married 60K No 7 Yes Divorced 220K No 8 No Single 85K Yes 9 No Married 75K No 10 No Single 90K Yes 10 categorical categorical continuous class
• 51. How to Estimate Probabilities from Data? • For continuous attributes: • Discretize the range into bins • one ordinal attribute per bin • violates independence assumption • Two-way split: (A < v) or (A > v) • choose only one of the two splits as new attribute • Probability density estimation: • Assume attribute follows a normal distribution • Use data to estimate parameters of distribution (i.e., mean  and standard deviation ) • Once probability distribution is known, we can use it to estimate the conditional probability P(Ai|c)
• 52. How to Estimate Probabilities from Data? • Normal distribution: • One for each (ai,ci) pair • For (Income, Class=No): • If Class=No • sample mean = 110 • sample variance = 2975 Tid Refund Marital Status Taxable Income Evade 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No 4 Yes Married 120K No 5 No Divorced 95K Yes 6 No Married 60K No 7 Yes Divorced 220K No 8 No Single 85K Yes 9 No Married 75K No 10 No Single 90K Yes 10 categorical categorical continuous class 2 2 2 ) ( 2 2 1 ) | ( ij ij a ij j i e c a A P        0072 . 0 ) 54 . 54 ( 2 1 ) | 120 ( ) 2975 ( 2 ) 110 120 ( 2      e No Income P 
• 53. Example of Naïve Bayes Classifier • Creating a Naïve Bayes Classifier, essentially means to compute counts: Total number of records: N = 10 Class No: Number of records: 7 Attribute Refund: Yes: 3 No: 4 Attribute Marital Status: Single: 2 Divorced: 1 Married: 4 Attribute Income: mean: 110 variance: 2975 Class Yes: Number of records: 3 Attribute Refund: Yes: 0 No: 3 Attribute Marital Status: Single: 2 Divorced: 1 Married: 0 Attribute Income: mean: 90 variance: 25
• 54. Example of Naïve Bayes Classifier P(Refund=Yes|No) = 3/7 P(Refund=No|No) = 4/7 P(Refund=Yes|Yes) = 0 P(Refund=No|Yes) = 1 P(Marital Status=Single|No) = 2/7 P(Marital Status=Divorced|No)=1/7 P(Marital Status=Married|No) = 4/7 P(Marital Status=Single|Yes) = 2/7 P(Marital Status=Divorced|Yes)=1/7 P(Marital Status=Married|Yes) = 0 For taxable income: If class=No: sample mean=110 sample variance=2975 If class=Yes: sample mean=90 sample variance=25 naive Bayes Classifier: 120K) Income Married, No, Refund (    X  P(X|Class=No) = P(Refund=No|Class=No)  P(Married| Class=No)  P(Income=120K| Class=No) = 4/7  4/7  0.0072 = 0.0024  P(X|Class=Yes) = P(Refund=No| Class=Yes)  P(Married| Class=Yes)  P(Income=120K| Class=Yes) = 1  0  1.2  10-9 = 0 P(No) = 0.3, P(Yes) = 0.7 Since P(X|No)P(No) > P(X|Yes)P(Yes) Therefore P(No|X) > P(Yes|X) => Class = No Given a Test Record:
• 55. Naïve Bayes Classifier • If one of the conditional probability is zero, then the entire expression becomes zero • Probability estimation: m N mp N c C a A P N N N c C a A P N N c C a A P c ac i i c ac i c ac i              ) | ( : estimate - m 1 ) | ( : Laplace ) | ( : Original Ni: number of attribute values for attribute Ai p: prior probability m: parameter
• 56. Example of Naïve Bayes Classifier P(Refund=Yes|No) = 4/9 P(Refund=No|No) = 5/9 P(Refund=Yes|Yes) = 1/5 P(Refund=No|Yes) = 4/5 P(Marital Status=Single|No) = 3/10 P(Marital Status=Divorced|No)=2/10 P(Marital Status=Married|No) = 5/10 P(Marital Status=Single|Yes) = 3/6 P(Marital Status=Divorced|Yes)=2/6 P(Marital Status=Married|Yes) = 1/6 For taxable income: If class=No: sample mean=110 sample variance=2975 If class=Yes: sample mean=90 sample variance=25 naive Bayes Classifier: 120K) Income Married, No, Refund (    X  P(X|Class=No) = P(Refund=No|Class=No)  P(Married| Class=No)  P(Income=120K| Class=No) = 5/9  5/10  0.0072  P(X|Class=Yes) = P(Refund=No| Class=Yes)  P(Married| Class=Yes)  P(Income=120K| Class=Yes) = 4/5  1/6  1.2  10-9 P(No) = 0.7, P(Yes) = 0.3 Since P(X|No)P(No) > P(X|Yes)P(Yes) Therefore P(No|X) > P(Yes|X) => Class = No Given a Test Record: With Laplace Smoothing
• 57. Implementation details • Computing the conditional probabilities involves multiplication of many very small numbers • Numbers get very close to zero, and there is a danger of numeric instability • We can deal with this by computing the logarithm of the conditional probability log 𝑃 𝐶 𝐴 ~ log 𝑃 𝐴 𝐶 + log 𝑃 𝐴 = 𝑖 log 𝐴𝑖 𝐶 + log 𝑃(𝐴)
• 58. Naïve Bayes for Text Classification • Naïve Bayes is commonly used for text classification • For a document 𝑑 = (𝑡1, … , 𝑡𝑘) 𝑃 𝑐 𝑑 = 𝑃(𝑐) 𝑡𝑖∈𝑑 𝑃(𝑡𝑖|𝑐) • 𝑃 𝑡𝑖 𝑐 = Fraction of terms from all documents in c that are 𝑡𝑖. • Easy to implement and works relatively well • Limitation: Hard to incorporate additional features (beyond words).
• 60. Naïve Bayes (Summary) • Robust to isolated noise points • Handle missing values by ignoring the instance during probability estimate calculations • Robust to irrelevant attributes • Independence assumption may not hold for some attributes • Use other techniques such as Bayesian Belief Networks (BBN) • Naïve Bayes can produce a probability estimate, but it is usually a very biased one • Logistic Regression is better for obtaining probabilities.
• 61. Generative vs Discriminative models • Naïve Bayes is a type of a generative model • Generative process: • First pick the category of the record • Then given the category, generate the attribute values from the distribution of the category • Conditional independence given C • We use the training data to learn the distribution of the values in a class C 𝐴1 𝐴2 𝐴𝑛
• 62. Generative vs Discriminative models • Logistic Regression and SVM are discriminative models • The goal is to find the boundary that discriminates between the two classes from the training data • In order to classify the language of a document, you can • Either learn the two languages and find which is more likely to have generated the words you see • Or learn what differentiates the two languages.
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