Data mining classification-2009-v0

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Data mining classification-2009-v0

  1. 1. Data Mining Classification Prithwis Mukerjee, Ph.D.
  2. 2. Prithwis Mukerjee 2 Classification Definition  The separation or ordering of objects ( or things ) in classes A Priori Classification  When the classification is done before you have looked at the data Post Priori Classification  When the classification is done after you have looked at the data
  3. 3. Prithwis Mukerjee 3 General approach You decide on the classes without looking at the data  For example : High risk, medium risk, low risk classes You “train” system  Take a small set of objects – the training set  Each object has a set of attributes  Classify the objects in this small (“training”) set into the three classes, without looking at the attributes  You will need human expertise here, to classify objects  Now find a set of rules based on the attributes such that the system classifies the objects just as you have done without looking at the attributes Use these rules to classify the full set of attributes
  4. 4. Prithwis Mukerjee 4 If we have this data ... Name Eggs Pouch Flies Feathers Class Cockatoo Yes No Yes Yes Bird No No No No Mammal Yes Yes No No Marsupial Emu Yes No No Yes Bird Kangaroo No Yes No No Marsupial Koala No Yes No No Marsupial Yes No Yes Yes Bird Owl Yes No Yes Yes Bird Penguin Yes No No Yes Bird Platypus Yes No No No Mammal Possum No Yes No No Marsupial Wombat No Yes No No Marsupial Dugong Echidna Kokkabura
  5. 5. Prithwis Mukerjee 5 We need to build a decision tree like .... Pouch ?Pouch ? Feathers ?Feathers ? Bird Mammal Marsupial YES YES NO NO
  6. 6. Prithwis Mukerjee 6 Question is ... Why did we ignore two attributes ?  Flies ?  Feathers ? Why did we use the attribute called POUCH first ?  And then we used the attribute called FEATHERS A rigorous classification process should tell us  If there are lots of attributes to be looked at then which are the important ones ?  In which order should we look at the attributes So that the classification arrived at is very similar to the classification done with the training set
  7. 7. Prithwis Mukerjee 7 Decision Tree : Tree Induction Algorithm Step 1 : Place all members into one node  If all members belong to the same class  Stop : there is nothing to be done Step 2 : Else  Choose one attribute and based on its value split the node into two nodes  For each of the two nodes  If all members belong to the same class  Stop  Else : Recursively go to Step 1 Big question : How do you choose which attribute to split a node on ?  Information Theory  GINI Index
  8. 8. Prithwis Mukerjee 8 Information Theory : Recapitulate Information Content I  Of an event E  That has n possible outcomes  Where outcome i happens with probability pi  Is defined as I = Σi ( - pi log2 pi ) Example :  Event EA has two possible outcomes  P1 = 0, P2 = 0 : Outcome 1 is a certainty  I = 0 because there is NO information in the outcome  Event EB has two possible outcomes  P1 = 0.5, P2 = 0.5 : Both outcomes are equally likely  I = -0.5 log2 (0.5) – 0.5 log2 (0.5) = 1  Maximum possible information that is possible for an event with two outcomes
  9. 9. Prithwis Mukerjee 9 Information in the roll of a dice Fair dice  All numbers 1 – 6 equally probable ( pi = 1/6)  I = 6 x (- 1/6) log2 (1/6) = 2.585 Loaded Dice Case 1  P6 = 0.5; P1 = P2 = P3 = P4 = P5 = 0.1  I = 5 x (-0.1) log2 (0.1) – 0.5 x log2 (0.5) = 2.16 Loaded Dice Case 2  P6 = 0.75; P1 = P2 = P3 = P4 = P5 = 0.05  I = 5 x (-0.05) log2 (0.1) – 0.75 x log2 (0.75) = 1.39 Point to note ...  We can change the information in the roll of the dice by changing the probabilities of the various outcomes !
  10. 10. Prithwis Mukerjee 10 How do we change the information ? In a dice  We make mechanical modifications so that the probabilities of each outcome changes  This is higly illegal In a set of individuals  We regroup the individuals into the classes so that the probability of each class changes  This is highly permitted in our algorithm H
  11. 11. Prithwis Mukerjee 11 Consider the following scenario .. Probability of each outcome ( or class )  P(A) = 3/10 , P(B) = 3/10 , P(C) = 4/10 Total Information Content of Set S  -(3/10) log2 (3/10) – (3/10) log2 (3/10) – (4/10) log2 (4/10) = 1.57 ID Home Married Gender Employed Credit Class 1 Yes Yes Male Yes A B 2 No No Female Yes A A 3 Yes Yes Female Yes B C 4 Yes No Male No B B 5 No Yes Female Yes B C 6 No No Female Yes B A 7 No No Male No B B 8 Yes No Female Yes A A 9 No Yes Female Yes A C 10 Yes Yes Female Yes A C
  12. 12. Prithwis Mukerjee 12 Suppose we split this set on HOME I1 : Information in set S1  -(2/5)log2 (2/5) – (1/5) log2 (1/5) – (2/5) log2 (2/5) = 1.52 I2 : Information in set S2  -(1/5)log2 (1/5) – (2/5) log2 (2/5) – (2/5) log2 (2/5) = 1.52 Total Information in S1 and S2  0.5 I1 + 0.5I2 = 0.5 x 1.52 + 0.5 x 1.52 = 1.52 ID Home Married Gender Employed Credit Class 2 No No Female Yes A A 5 No Yes Female Yes B C 6 No No Female Yes B A 7 No No Male No B B 9 No Yes Female Yes A C ID Home Married Gender Employed Credit Class 1 Yes Yes Male Yes A B 3 Yes Yes Female Yes B C 4 Yes No Male No B B 8 Yes No Female Yes A A 10 Yes Yes Female Yes A C P1 (A) = 2/5 P1 (B) = 1/5 P1 (C) = 2/5 P2 (A) = 1/5 P2 (B) = 2/5 P2 (C) = 2/5
  13. 13. Prithwis Mukerjee 13 Impact of HOME attribute In sets S1 and S2 , the attribute HOME was the same But in set S the attribute HOME is not the same and so is of some significance What is the significance of the HOME attribute ? By adding the HOME attribute we have increased the information content  FROM : 1.52  TO : 1.57 So HOME attribute adds 0.05 to the overall information content  Or HOME attribute reduces uncertainty by 0.05
  14. 14. Prithwis Mukerjee 14 Let us go back to the original set S .. Probability of each outcome ( or class )  P(A) = 3/10 , P(B) = 3/10 , P(C) = 4/10 Total Information Content of Set S  -(3/10) log2 (3/10) – (3/10) log2 (3/10) – (4/10) log2 (4/10) = 1.57 ID Home Married Gender Employed Credit Class 1 Yes Yes Male Yes A B 2 No No Female Yes A A 3 Yes Yes Female Yes B C 4 Yes No Male No B B 5 No Yes Female Yes B C 6 No No Female Yes B A 7 No No Male No B B 8 Yes No Female Yes A A 9 No Yes Female Yes A C 10 Yes Yes Female Yes A C
  15. 15. Prithwis Mukerjee 15 This time we split on GENDER I1 : Information in set S1  -(3/7)log2 (3/7) – (4/7) log2 (4/7) = 0.985 I2 : Information in set S2  = 0 Total Information in S1 and S2  (7/10) I1 + (3/10)I2 = 7/10 x 0.985 + 3/10 x 0 = 0.69 ID Home Married Gender Employed Credit Class 2 No No Female Yes A A 3 Yes Yes Female Yes B C 5 No Yes Female Yes B C 6 No No Female Yes B A 8 Yes No Female Yes A A 9 No Yes Female Yes A C 10 Yes Yes Female Yes A C ID Home Married Gender Employed Credit Class 1 Yes Yes Male Yes A B 4 Yes No Male No B B 7 No No Male No B B P1 (A) = 3/7 P1 (B) = 0/7 P1 (C) = 4/7 P2 (A) = 0/3 P2 (B) = 3/3 P2 (C) = 0/3
  16. 16. Prithwis Mukerjee 16 Impact of GENDER attribute In sets S1 and S2 , the attribute GENDER was the same But in set S the attribute GENDER is not the same and so is of some significance What is the significance of the GENDER attribute ? By adding the GENDER attribute we have increased the information content  FROM : 0.69  TO : 1.57 So GENDER attribute adds 0.88 to the overall information content  Or GENDER attribute reduces uncertainty by 0.88
  17. 17. Prithwis Mukerjee 17 If we were to do this for all attributes ... We would observe that GENDER is the best candidate for the split Attribute Home 1.57 1.52 0.05 Married 1.57 0.85 0.72 Gender 1.57 0.69 0.88 Employed 1.57 1.12 0.45 Credit 1.57 1.52 0.05 Information before Split Information after Split Information Gain
  18. 18. Prithwis Mukerjee 18 And the first part of our tree would be ... GenderGender What Next ?What Next ? Class B MaleFemale
  19. 19. Prithwis Mukerjee 19 Remove GENDER and Class B and continue ID Home Married Employed Credit Class 2 No No Yes A A 3 Yes Yes Yes B C 5 No Yes Yes B C 6 No No Yes B A 8 Yes No Yes A A 9 No Yes Yes A C 10 Yes Yes Yes A C Probability of each outcome ( or class )  P(A) = 3/7 , P(C) = 4/7 Total Information Content of Set S  -(3/7) log2 (3/7) – (4/7) log2 (4/7) = 1.33
  20. 20. Prithwis Mukerjee 20 We split this set on HOME ... I1 : Information in set S1  -(2/4)log2 (2/4) – (2/4) log2 (2/4) = 1.00 I2 : Information in set S2  -(1/3)log2 (1/3) – (2/3) log2 (2/3) = 0.92 Total Information in S1 and S2  (4/7) I1 + (3/7)I2 = 4/7 x 1.00 + 3/7 x 0.92 = 0.96 ID Home Married Employed Credit Class 2 No No Yes A A 5 No Yes Yes B C 6 No No Yes B A 9 No Yes Yes A C ID Home Married Employed Credit Class 3 Yes Yes Yes B C 8 Yes No Yes A A 10 Yes Yes Yes A C P1 (A) = 2/4 P1 (C) = 2/4 P1 (A) = 1/3 P1 (C) = 2/3 Gain = 1.33 – 0.96 = 0.37
  21. 21. Prithwis Mukerjee 21 But if we were to split on MARRIED I1 : Information in set S1  = 0.0 I2 : Information in set S2  = 0.0 Total Information in S1 and S2  = 0.0 ID Home Married Employed Credit Class 2 No No Yes A A 8 Yes No Yes A A 6 No No Yes B A ID Home Married Employed Credit Class 3 Yes Yes Yes B C 9 No Yes Yes A C 10 Yes Yes Yes A C 5 No Yes Yes B C P1 (A) = 4/4 P1 (C) = 0/4 P1 (A) = 0/3 P1 (C) = 3/3 Gain = 1.33 - 0 = 1.33
  22. 22. Prithwis Mukerjee 22 Two things have happened With MARRIED  We have hit the upper limit of information gain  No other attribute can do any better than this In The TWO sub sets  All members belong to the same class  Either A or C Hence we STOP here and observe ...
  23. 23. Prithwis Mukerjee 23 That our DECISION TREE looks like GenderGender MarriedMarried Class C Class A Class B Male YES Female NO
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