CLASSIFICATION: DECISION TREES
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1 CLASSIFICATION: DECISION TREES Gökhan Akçapınar Seminar in Methodology and Statistics John Nerbonne, Çağrı Çöltekin University of Groningen May, 2012
2 Outline Research question Background knowledge Data collection Classification with decision trees R example
3 Research Problem Predict student performance based on their activity data on wiki environment.
4 Wikis «A wiki is a website whose users can add, modify, or delete its content via a web browser.»
5 Wiki software Wikis are typically powered by wiki software and are often created collaboratively by multiple users.
6 Wiki in Education Wikis are using mostly in group work and collaboration. Students create content, knowledge production
7 Assessment in Wiki? Assessment and to rate individual performance are the main problems in introducing wikis. If teachers cannot assess wiki work, we can not expect wiki to be adopted for education, despite the potential learning gains for students.
8 Why assessment is difficult? Sample wiki page
9 History Logs / Revisions
10 WikLog
11 WikLog
12 WikLog
13 Metrics (Attributes) PageCount: The number of pages created by the user. EditCount: The number of edits conducted by the user. LinkCount: The number of links created by the user. WordCount: The number of words created by the user.
14 Sample Data ID PageCount EditCount LinkCount WordCount Final Grade 1 55,00 334,00 30, ,00 B1 2 5,00 194,00 0,00 430,00 F 3 37,00 267,00 243, ,00 A1 4 75,00 402,00 138, ,00 A2 5 24,00 183,00 1,00 2,00 F 6 40,00 232,00 83, ,00 C1 7 8,00 128,00 13, ,00 F 8 28,00 283,00 29, ,00 B2 9 27,00 99,00 10,00 432,00 D ,00 113,00 9, ,00 F
15 Class / Output Variable A1 A2 B1 High ID Final Grade 1 B1 2 F 3 A1 ID Performance 1 High 2 Low 3 High B2 C1 C2 D1 Medium 4 A2 5 F 6 C1 7 F 4 High 5 Low 6 Medium 7 Low D2 F2 F3 Low 8 B2 9 D2 10 F 8 Medium 9 Low 10 Low
16 Research Problem ID PageCount EditCount LinkCount WordCount Performance 1 55,00 334,00 30, ,00 High 2 5,00 194,00 0,00 430,00 Low 3 37,00 267,00 243, ,00 High 4 75,00 402,00 138, ,00 High 5 24,00 183,00 1,00 2,00 Low 6 40,00 232,00 83, ,00 Medium 7 8,00 128,00 13, ,00 Low 8 28,00 283,00 29, ,00 Medium 9 27,00 99,00 10,00 432,00 Low 10 32,00 113,00 9, ,00 Low
17 Research Problem ID PageCount EditCount LinkCount WordCount Performance 1 55,00 334,00 30, ,00 High 2 5,00 194,00 0,00 430,00 Low 3 37,00 267,00 243, ,00 High 4 75,00 402,00 138, ,00 High 5 24,00 183,00 1,00 2,00 Low 6 40,00 232,00 83, ,00 Medium 7 8,00 128,00 13, ,00 Low 8 28,00 283,00 29, ,00 Medium 9 27,00 99,00 10,00 432,00 Low ID PageCount EditCount LinkCount WordCount Performance 11 80,00 547,00 193, ,00? 12 65,00 271,00 273, ,00? 13 47,00 252,00 231, ,00? ,00 278,00 399, ,00? 15 55,00 266,00 49, ,00? 10 32,00 113,00 9, ,00 Low
18 Research Problem ID PageCount EditCount LinkCount WordCount Performance 1 55,00 334,00 30, ,00 High 2 5,00 194,00 0,00 430,00 Low 3 37,00 267,00 243, ,00 High 4 75,00 402,00 138, ,00 High 5 24,00 183,00 1,00 2,00 Low 6 40,00 232,00 83, ,00 Medium 7 8,00 128,00 13, ,00 Low 8 28,00 283,00 29, ,00 Medium 9 27,00 99,00 10,00 432,00 Low ID PageCount EditCount LinkCount WordCount Performance 11 80,00 547,00 193, ,00? 12 65,00 271,00 273, ,00? 13 47,00 252,00 231, ,00? ,00 278,00 399, ,00? 15 55,00 266,00 49, ,00? 10 32,00 113,00 9, ,00 Low
19 Prediction: Classification or Numeric Prediction? The objective of prediction is to estimate the unknown value of a variable. In education, the values can be knowledge, score, or mark. This value can be numerical/continuous value (regression task) or categorical/discrete value (classification task). Classification 1, 0, 0, 1. A, D, B, F. 3, 3, 1, 2. Numeric Prediction 23, 56, 87, 5
20 Classification Classification is a procedure in which individual items are placed into groups based on quantitative information regarding one or more characteristics inherent in the items and based on a training set of previously labeled items.
21 Classification A Two-Step Process Induction Tree Induction algorithm Learn Model Training Set Model Apply Model Decision Tree Deduction Test Set
22 Classification A Two-Step Process Model Construction Induction Tree Induction algorithm Learn Model Training Set Model Apply Model Decision Tree Deduction Test Set
23 Classification A Two-Step Process Induction Tree Induction algorithm Learn Model Training Set Model Apply Model Decision Tree Test Set Deduction Using the Model in Prediction
24 Classification Techniques Decision Tree based Methods Rule-based Methods Memory based reasoning Neural Networks Naïve Bayes and Bayesian Belief Networks Support Vector Machines
25 Classification Techniques Decision Tree based Methods
26 Example of a Decision Tree ID PageCount EditCount LinkCount WordCount Performance 1 55,00 334,00 30, ,00 High Splitting Attributes 2 5,00 194,00 0,00 430,00 Low 3 37,00 267,00 243, ,00 High 4 75,00 402,00 138, ,00 High 5 24,00 183,00 1,00 2,00 Low 6 40,00 232,00 83, ,00 Medium 7 8,00 128,00 13, ,00 Low 8 28,00 283,00 29, ,00 Medium 9 27,00 99,00 10,00 432,00 Low 10 32,00 113,00 9, ,00 Low Edit < 200 > 200 Low Word < 3000 Medium > 3000 High Training Data Model: Decision Tree
27 Example of a Decision Tree ID PageCount EditCount LinkCount WordCount Performance 1 55,00 334,00 30, ,00 High Splitting Attributes 2 5,00 194,00 0,00 430,00 Low 3 37,00 267,00 243, ,00 High 4 75,00 402,00 138, ,00 High 5 24,00 183,00 1,00 2,00 Low 6 40,00 232,00 83, ,00 Medium 7 8,00 128,00 13, ,00 Low 8 28,00 283,00 29, ,00 Medium 9 27,00 99,00 10,00 432,00 Low 10 32,00 113,00 9, ,00 Low Page > 55 < 55 High Link > 20 Medium < 20 Low Training Data Model: Decision Tree
28 Example of Apply Model to Test Data Induction Tree Induction algorithm Learn Model Training Set Model Apply Model Decision Tree Deduction Test Set Using the Model in Prediction
29 Apply Model to Test Data Start from the root of tree. Test Data ID PageCount EditCount LinkCount WordCount Performance 15 55,00 266,00 49, ,00? Edit < 200 > 200 Low < 3000 Word > 3000 Medium High
30 Apply Model to Test Data Test Data ID PageCount EditCount LinkCount WordCount Performance 15 55,00 266,00 49, ,00? Edit < 200 > 200 Low < 3000 Word > 3000 Medium High
31 Apply Model to Test Data Test Data ID PageCount EditCount LinkCount WordCount Performance 15 55,00 266,00 49, ,00? Edit < 200 > 200 Low < 3000 Word > 3000 Medium High
32 Apply Model to Test Data Test Data ID PageCount EditCount LinkCount WordCount Performance 15 55,00 266,00 49, ,00? Edit < 200 > 200 Low < 3000 Word > 3000 Medium High
33 Apply Model to Test Data Test Data ID PageCount EditCount LinkCount WordCount Performance 15 55,00 266,00 49, ,00? Edit < 200 > 200 Low < 3000 Word > 3000 Medium High
34 Apply Model to Test Data Test Data ID PageCount EditCount LinkCount WordCount Performance 15 55,00 266,00 49, ,00? Edit < 200 > 200 Low < 3000 Word > 3000 Medium High
35 Choosing the Splitting Attribute Typical goodness functions: information gain (ID3/C4.5) information gain ratio gini index Which is the best attribute? The one which will result in the smallest tree Choose the attribute that produces the purest nodes Strategy: choose attribute that results in greatest information gain
36 Information Gain (ID3/C4.5) Select the attribute with the highest information gain Expected information (entropy) needed to classify a tuple in D: Info( D) p i log 2( p Information needed (after using A to split D into v partitions) to classify D: m i 1 i ) Info A ( D) v Dj D j 1 Info( D j ) Information gained by branching on attribute A Gain(A) Info(D) Info A (D)
37 When do I play tennis? Outlook Temperature Humidity Windy Play? sunny hot high false No sunny hot high true No overcast hot high false Yes rain mild high false Yes rain cool normal false Yes rain cool normal true No overcast cool normal true Yes sunny mild high false No sunny cool normal false Yes rain mild normal false Yes sunny mild normal true Yes overcast mild high true Yes overcast hot normal false Yes rain mild high true No
38 Example Tree for Play? Outlook sunny overcast rain Humidity Yes Windy high normal false true No Yes No Yes
39 Which attribute to select?
40 Example: attribute Outlook, 2 Outlook = Sunny : info([2,3] ) entropy(2/5,3/5) 2/5log(2/5) 3/ 5log(3/ 5) 0.971bits Outlook = Overcast : info([4,0] ) entropy(1,0) 1log(1) 0log(0) 0 bits Outlook = Rainy : info([3,2] ) entropy(3/5,2/5) 3/ 5log(3/ 5) 2/5log(2/5) 0.971bits Expected information for attribute: info([3,2],[4,0],[3,2]) (5/14) (4/14) 0 (5/14) bits
41 Computing the information gain Information gain: (information before split) (information after split) gain(" Outlook") info([9,5]) -info([2,3],[4,0],[3,2]) bits Compute for attribute Humidity
42 Example: attribute Humidity Humidity = High : info([3,4] ) entropy(3/ 7,4/7) 3/ 7log(3/ 7) 4/7log(4/7) bits Humidity = Normal : info([6,1] ) entropy(6/ 7,1/7) Expected information for attribute: 6/7log(6/7) 1/ 7log(1/ 7) bits info([3,4],[6,1]) (7/14) (7/14) bits Information Gain: info([9,5] ) -info([3,4],[6,1])
43 Computing the information gain Information gain for attributes from weather data: gain(" Outlook") gain(" Temperature") gain(" Humidity") gain(" Windy") bits bits bits bits Outlook sunny overcast rain
44 Continuing to split gain(" Temperature") 0.571bits gain(" Humidity") 0.971bits gain(" Windy") bits
45 The final decision tree Splitting stops when data can t be split any further
46 Rpart() install.packages('rpart') library(rpart) data = read.xls("c://tree_data.xls",colnames = TRUE) results = rpart(performance~pagecount+editcount+linkcount+wordco unt, data=data, method="class", parms=list(split='information')) printcp(results) plot(results) text(results)
47 CLASSIFICATION: DECISION TREES Gökhan Akçapınar Seminar in Methodology and Statistics John Nerbonne, Çağrı Çöltekin University of Groningen May, 2012
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