Knowledge Discovery and Data Mining
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1 Knowledge Discovery and Data Mining Unit # 4 1 Acknowledgement Most of the slides in this presentation are taken from course slides provided by Han and Kimber(Data Mining Concepts and Techniques) and Tan, Steinbach and Kumar (Introduction to Data Mining) 2 1
2 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 unseenrecords should be assigned a class as accurately as possible. A test setis 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. 3 Classification: Motivation age income student credit_rating buys_computer <=30 high no fair no <=30 high no excellent no high no fair yes >40 medium no fair yes >40 low yes fair yes >40 low yes excellent no low yes excellent yes <=30 medium no fair no <=30 low yes fair yes >40 medium yes fair yes <=30 medium yes excellent yes medium no excellent yes high yes fair yes >40 medium no excellent no 4 2
3 /20/2011 Decision/Classification Tree age? <=30 overcast >40 student? yes credit rating? no yes excellent fair no yes yes 5 Illustrating Classification Task 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 Learn Model 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? Apply Model 6 3
4 /20/2011 Example of a 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 7 Another Example of Decision Tree 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 Married MarSt Yes Single, Divorced Refund No TaxInc < 80K > 80K YES There could be more than one tree that fits the same data! 8 4
5 /20/2011 Decision Tree Classification Task 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 Learn Model 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? Apply Model Decision Tree 9 Apply Model to Test Data Start from the root of tree. Test Data Refund Marital Status Taxable Income Cheat Yes Refund No No Married 80K? Single, Divorced MarSt Married TaxInc < 80K > 80K YES 10 5
6 /20/2011 Apply Model to Test Data Test Data Refund Marital Status Taxable Income Cheat Yes Refund No No Married 80K? Single, Divorced MarSt Married TaxInc < 80K > 80K YES 11 Apply Model to Test Data Test Data Refund Marital Status Taxable Income Cheat Yes Refund No No Married 80K? Single, Divorced MarSt Married TaxInc < 80K > 80K YES 12 6
7 /20/2011 Apply Model to Test Data Test Data Refund Marital Status Taxable Income Cheat Yes Refund No No Married 80K? Single, Divorced MarSt Married TaxInc < 80K > 80K YES 13 Apply Model to Test Data Test Data Refund Marital Status Taxable Income Cheat Yes Refund No No Married 80K? Single, Divorced MarSt Married TaxInc < 80K > 80K YES 14 7
8 /20/2011 Apply Model to Test Data Test Data Refund Marital Status Taxable Income Cheat Yes Refund No No Married 80K? Single, Divorced MarSt Married Assign Cheat to No TaxInc < 80K > 80K YES 15 Decision Tree Classification Task 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 Learn Model 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? Apply Model Decision Tree 16 8
9 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 17 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 18 9
10 How to determine the Best Split Greedy approach: Nodes with homogeneousclass distribution are preferred Need a measure of node impurity: Non-homogeneous, High degree of impurity Homogeneous, Low degree of impurity 19 Measures of Node Impurity Gini Index Entropy Misclassification error 20 10
11 Measure of Impurity: GINI Gini Index for a given node t : GINI ( t) = 1 (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 j [ p( j t)] 2 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= Examples for computing GINI GINI( t) = 1 j [ p( j t)] 2 C1 0 C2 6 P(C1) = 0/6 = 0 P(C2) = 6/6 = 1 Gini = 1 P(C1) 2 P(C2) 2 = = 0 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 =
12 Binary Attributes: Computing GINI Index Splits into two partitions Effect of Weighing partitions: Larger and Purer Partitions are sought for. Gini(N1) = 1 (6/7) 2 (1/7) 2 = 0.24 Gini(N2) = 1 (3/7) 2 (4/7) 2 = 0.49 Sajjad Haider Yes Node N1 Student? N1 N2 Yes 6 3 No 1 4 Gini=0.365 No Node N2 Buy Computer Yes 9 No 5 Gini(Student) = 7/14 * /14 * 0.49 =?? Gini = 0.46 GINI Index for Buy Computer Example Gini (Income): Gini (Credit_Rating): Gini (Age): 24 12
13 Alternative Splitting Criteria based on Entropy Entropy at a given node t: Entropy ( t) = p( j t)log p( j t) j (TE: p( j t) is the relative frequency of class j at node t). Measures homogeneity of a node. Maximum (log n c ) when records are equally distributed among all classes implying least information Minimum (0.0) when all records belong to one class, implying most information Entropy based computations are similar to the GINI index computations 25 Entropy in a nut-shell Low Entropy High Entropy 26 13
14 Examples for computing Entropy Entropy t) = p( j t)log p( j t) j ( 2 C1 0 C2 6 P(C1) = 0/6 = 0 P(C2) = 6/6 = 1 Entropy = 0 log 0 1 log 1 = 0 0 = 0 C1 1 C2 5 P(C1) = 1/6 P(C2) = 5/6 Entropy = (1/6) log 2 (1/6) (5/6) log 2 (1/6) = 0.65 C1 2 C2 4 P(C1) = 2/6 P(C2) = 4/6 Entropy = (2/6) log 2 (2/6) (4/6) log 2 (4/6) = Splitting Criteria based on Classification Error Classification error at a node t : Error( t) = 1 max P( i t) i Measures misclassification error made by a node. 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 28 14
15 Examples for Computing Error Error( t) = 1 max P( i t) i C1 0 C2 6 P(C1) = 0/6 = 0 P(C2) = 6/6 = 1 Error = 1 max (0, 1) = 1 1 = 0 C1 1 C2 5 P(C1) = 1/6 P(C2) = 5/6 Error = 1 max (1/6, 5/6) = 1 5/6 = 1/6 C1 2 C2 4 P(C1) = 2/6 P(C2) = 4/6 Error = 1 max (2/6, 4/6) = 1 4/6 = 1/3 29 Comparison among Splitting Criteria For a 2-class problem: 30 15
16 Inducing a decision tree There are many possible trees How to find the most compact one that is consistent with the data? The keyto building a decision tree -which attribute to choose in order to branch. The heuristicis to choose the attribute with the minimum GINI/Entropy. 31 Algorithm for Decision Tree Induction Basic algorithm (a greedy algorithm) Tree is constructed in a top-down recursive manner At start, all the training examples are at the root Attributes are categorical Examples are partitioned recursively based on selected attributes Test attributes are selected on the basis of a heuristic or statistical measure (e.g., GINI/Entropy) Conditions for stopping partitioning All examples for a given node belong to the same class There are no remaining attributes for further partitioning majority voting is employed for classifying the leaf There are no examples left 32 16
17 Extracting Classification Rules from Trees Represent the knowledge in the form of IF-THEN rules One rule is created for each path from the root to a leaf Each attribute-value pair along a path forms a conjunction. The leaf node holds the class prediction Rules are easier for humans to understand Example IF age= <=30 AND student= no THEN buys_computer= no IF age= <=30 AND student= yes THEN buys_computer= yes IF age = THEN buys_computer = yes IF age = >40 AND credit_rating = excellent THEN buys_computer = yes IF age = <=30 AND credit_rating = fair THEN buys_computer = no 33 Questions? Why the method to generate a classification tree is a heuristic and not a guaranteed method? Hint: Think of a situation where a is the best attribute, but the combination of b and c would actually be better than any of a and b, or a and c. That is, knowing b and c you can classify, but knowing only a and b (or only a and c) you cannot. This shows that the attributes may not be independent. How could we deal with this? Hint: Consider also combination of attributes, not only a, b, c, but also ab, bc, ca What is a problem with this approach? 34 17
18 Example Attribute 1 Attribute 2 Attribute 3 Class A 70 T C1 A 90 T C2 A 85 F C2 A 95 F C2 A 70 F C1 B 90 T C1 B 78 F C1 B 65 T C1 B 75 F C1 C 80 T C2 C 70 T C2 C 80 F C1 C 80 F C1 C 96 F C1 35 Example II Height Hair Eyes Class Short Blond Blue + Tall Blond Brown - Tall Red Blue + Short Dark Blue - Tall Dark Blue - Tall Blond Blue + Tall Dark Brown - Short Blond Brown
19 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 37 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) 38 19
20 Characteristics of Decision Tree Induction Decision tree induction is a non-parametric approach for building classification models. In other words, it doesn t require any prior assumptions regarding the type of probability distributions satisfied by the class and other attributes. Finding an optimal decision tree is an NP-complete problem. Many decision tree algorithms emplya heuristic-based approach to guide their search in the vast hypothesis space. For example, the algorithm discussed in this unit uses a greedy, top-down, recursive partitioning startegyfor growing a decision tree. 39 Characteristics of Decision Tree Induction (Cont d) Techniques developed for constructing decision trees are computationally inexpensive, making it possible to quickly construct models even when the training set size is very large. Furthermore, once a decision tree has been built, classifying a test record is extremely fast, with a worst-case complexity of O(w), where w is the maximum depth of the tree. Decision tree, specially smaller-sized trees, are relatively easy to interpret. Decision tree algorithms are quite robust to the presence of noise
21 Characteristics of Decision Tree Induction (Cont d) The presence of redundant attributes does not adversely affect the accuracy of decision trees. An attribute is redundant if it is strongly correlated with another attribute in the data. One of the two redundnantattributes will not be used for splitting once the other attribute has been chosen. Studies have shown that the choice of impurity measures has little effect on the performance of decision tree induction algorithms. 41 Advantages of Decision Tree Based Classification 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 42 21
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