Data Mining Concepts & Techniques

Transcription

1 Data Mining Concepts & Techniques Lecture No. 03 Data Processing, Data Mining Naeem Ahmed Department of Software Engineering Mehran Univeristy of Engineering and Technology Jamshoro

2 Data Mining: Classification Outline Basic concepts Decision Tree Hunt s Algorithm Acknowledgements: Introduction to Data Mining Tan, Steinbach, Kumar

3 Classification: Definition Given a collection of records (training set ) Each record contains a set of attributes, one of the attributes is the class Find a model for class attribute as a function of the values of other attributes Goal: previously unseen records should be assigned a class as accurately as possible A test set is used to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it

4 Classification: Definition Classification is a task of learning a target function f that maps each attribute set x to one of the predefined class label y. The target function is formally known as Classification Model, having purposes: Descriptive Modeling: serves as explanatory tool to distinguish between objects of different classes E.g., Mammal Class for (human, whale) Predictive Modeling: serves to predict the class label of unknown records E.g., Predicting possible Remarks for a student based on known patterns of other students

5 10 10 Classification 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 Training Set 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? Test Set Induction Deduction Learning algorithm Learn Model Apply Model Model

6 Evaluation Metrics Precision: exactness what % of records that classifier labeled as positive as actually positive Recall: completeness what % of positive records did classifier labeled as positive. Perfect score 1.0 F-measure: harmonic mean of precision and recall Higher the F-measure value, better is the classifier classifies

7 Evaluation Metrics TP: If the instance is positive and it is classified as positive, it is counted as a true positive (TP) Confusion Matrix FN: If the instance is positive and it is classified as negative, it is counted as a false negative (FN). TN: If the instance is negative and it is classified as negative, it is counted as a true negative (TN). FP: If the instance is negative and it is classified as positive, it is counted as a false positive (FN). N is the total true Negatives and P is the total true positives

8 Classification: Examples Classifying credit card transactions as legitimate or fraudulent Classifying secondary structures of protein as alpha-helix, beta-sheet, or random coil Categorizing news stories as finance, weather, entertainment, sports, etc

9 Classification Techniques Decision Tree based Methods Rule-based Methods Memory based reasoning Neural Networks Naïve Bayes and Bayesian Belief Networks Support Vector Machines

10 Decision Tree An inductive learning task Use particular facts to make more generalized conclusions A predictive model based on a branching series of Boolean tests These smaller Boolean tests are less complex than a one-stage classifier

11 10 Decision Tree Tid Refund Marital Status Taxable Income Cheat Splitting Attributes 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 Refund Yes No MarSt Single, Divorced TaxInc < 80K > 80K YES Married Training Data Model: Decision Tree

12 10 Decision Tree: Example Start from the root of tree. Test Data Refund Marital Status Taxable Income Cheat Refund No Married 80K? Yes No Single, Divorced MarSt Married TaxInc < 80K > 80K YES

13 10 Decision Tree: Example Test Data Refund Marital Status Taxable Income Cheat Yes Refund No No Married 80K? Single, Divorced MarSt Married TaxInc < 80K > 80K YES

14 10 Decision Tree: Example Test Data Refund Marital Status Taxable Income Cheat Yes Refund No No Married 80K? Single, Divorced MarSt Married TaxInc < 80K > 80K YES

15 10 Decision Tree: Example Test Data Refund Marital Status Taxable Income Cheat Yes Refund No No Married 80K? Single, Divorced MarSt Married TaxInc < 80K > 80K YES

16 10 Decision Tree: Example Test Data Refund Marital Status Taxable Income Cheat Yes Refund No No Married 80K? Single, Divorced MarSt Married TaxInc < 80K > 80K YES

17 10 Decision Tree: Example Test Data Refund Marital Status Taxable Income Cheat Refund No Married 80K? Yes No Single, Divorced MarSt Married Assign Cheat to No TaxInc < 80K > 80K YES

18 Decision Tree Algorithms The basic idea behind any decision tree algorithm is as follows: Choose the best attribute(s) to split the remaining instances and make that attribute a decision node Repeat this process for recursively for each child Stop when: All the instances have the same target attribute value There are no more attributes There are no more instances

19 Decision Tree Algorithms Many Algorithms: Hunt s Algorithm (one of the earliest) CART ID3, C4.5 SLIQ,SPRINT

20 10 Hunt s Algorithm Let D t be the set of training records that reach a node t General Procedure: If D t contains records that belong the same class y t, then t is a leaf node labeled as y t If D t is an empty set, then t is a leaf node labeled by the default class, y d If D t contains records that belong to more than one class, use an attribute test to split the data into smaller subsets. Recursively apply the procedure to each subset Tid Refund Marital Status Taxable Income 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No 4 Yes Married 120K No Cheat 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? D t

21 10 Hunt s Algorithm Don t Cheat Refund Yes Don t Cheat Single, Divorced Cheat Refund Yes Don t Cheat No Marital Status Married Don t Cheat No Don t Cheat Refund Yes Don t Cheat Single, Divorced Don t Cheat Taxable Income No Marital Status < 80K >= 80K Cheat Married Don t Cheat Tid Refund Marital Status Taxable Income 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No 4 Yes Married 120K No Cheat 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

22 Tree Induction Greedy strategy Split the records based on an attribute test that optimizes certain criterion Issues Determine how to split the records How to specify the attribute test condition? How to determine the best split? Determine when to stop splitting

23 How to Specify Test Condition? Depends on attribute types Nominal Ordinal Continuous Depends on number of ways to split 2-way split Multi-way split

24 Splitting Based on Nominal Attributes Multi-way split: Use as many partitions as distinct values Family CarType Sports Luxury Binary split: Divides values into two subsets. Need to find optimal partitioning {Sports, Luxury} CarType {Family} OR {Family, Luxury} CarType {Sports}

25 Splitting Based on Ordinal Attributes Multi-way split: Use as many partitions as distinct values Small Size Medium Large Binary split: Divides values into two subsets Need to find optimal partitioning {Small, Medium} Size {Large} OR {Medium, Large} Size {Small} What about this split? {Small, Large} Size {Medium}

26 Splitting Based on Continuous Attributes Different ways of handling Discretization to form an ordinal categorical attribute Static discretize once at the beginning Dynamic ranges can be found by equal interval bucketing, equal frequency bucketing (percentiles), or clustering. Binary Decision: (A < v) or (A v) consider all possible splits and finds the best cut can be more compute intensive

27 Splitting Based on Continuous Attributes Taxable Income > 80K? Taxable Income? < 10K > 80K Yes No [10K,25K) [25K,50K) [50K,80K) (i) Binary split (ii) Multi-way split

28 How to determine the Best Split Before Splitting: 10 records of class 0, 10 records of class 1 Own Car? Car Type? Student ID? Yes No Family Luxury c 1 c 10 c 20 Sports c 11 C0: 6 C1: 4 C0: 4 C1: 6 C0: 1 C1: 3 C0: 8 C1: 0 C0: 1 C1: 7 C0: 1 C1: 0... C0: 1 C1: 0 C0: 0 C1: 1... C0: 0 C1: 1 Which test condition is the best?

29 How to determine the Best Split Greedy approach: Nodes with homogeneous class distribution are preferred Need a measure of node impurity: C0: 5 C1: 5 Non-homogeneous, High degree of impurity C0: 9 C1: 1 Homogeneous, Low degree of impurity

30 Gini Index Measures of Node Impurity Entropy Misclassification error

31 How to Find the Best Split A? Before Splitting: C0 C1 N00 N01 M0 B? Yes No Yes No Node N1 Node N2 Node N3 Node N4 C0 N10 C0 N20 C0 N30 C0 N40 C1 N11 C1 N21 C1 N31 C1 N41 M1 M2 M3 M4 M12 Gain = M0 M12 vs M0 M34 M34

32 Measure of Impurity: GINI Gini Index for a given node t : GINI( t) = 1 j [ p( j t)] 2 (TE: p( j t) is the relative frequency of class j at node t). Maximum (1-1/n c ) when records are equally distributed among all classes, implying least interesting information Minimum (0.0) when all records belong to one class, implying most interesting information C1 0 C2 6 Gini=0.000 C1 1 C2 5 Gini=0.278 C1 2 C2 4 Gini=0.444 C1 3 C2 3 Gini=0.500

33 Examples for computing GINI C1 0 C2 6 GINI( t) = 1 j [ p( j t)] P(C1) = 0/6 = 0 P(C2) = 6/6 = 1 Gini = 1 P(C1) 2 P(C2) 2 = = 0 2 C1 1 C2 5 P(C1) = 1/6 P(C2) = 5/6 Gini = 1 (1/6) 2 (5/6) 2 = C1 2 C2 4 P(C1) = 2/6 P(C2) = 4/6 Gini = 1 (2/6) 2 (4/6) 2 = 0.444

34 Splitting Based on GINI Used in CART, SLIQ, SPRINT When a node p is split into k partitions (children), the quality of split is computed as, GINI split = k i= 1 ni n GINI( i) where, n i = number of records at child i, n = number of records at node p

35 Binary Attributes: Computing GINI Index Splits into two partitions Effect of Weighing partitions: Larger and Purer Partitions are sought for B? Parent Yes No C1 6 C2 6 Gini(N1) = 1 (5/6) 2 (2/6) 2 = Gini(N2) = 1 (1/6) 2 (4/6) 2 = Node N1 Node N2 N1 N2 C1 5 1 C2 2 4 Gini=0.333 Gini = Gini(Children) = 7/12 * /12 * = 0.333

36 Stopping Criteria for Tree Induction Stop expanding a node when all the records belong to the same class Stop expanding a node when all the records have similar attribute values Early termination (to be discussed later)

37 Decision Tree based Classification Advantages Inexpensive to construct Extremely fast at classifying unknown records Easy to interpret for small-sized trees Accuracy is comparable to other classification techniques for many simple data sets Practical issues Underfitting and Overfitting Missing Values Costs of Classification

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