1. TOPICS IN BUSINESS INTELLIGENCE K-NN & Naive Bayes – GROUP 1 Isabel van der Lijke Nathan Bok Gökhan Korkmaz

2. INTRODUCTION K-NN  k-NN Classifier (Categorical Outcome)  Determining Neighbors  Classification Rule  Example: Riding Mowers  Choosing k  Setting the Cutoff Value  Advantages and shortcomings of k-NN algorithms 2

3. INTRODUCTION NAIVE BAYES  Basic Classification Procedure  Cutoff Probability Method  Conditional Probability  Naive Bayes  Advantages and shortcomings of the naive Bayes classifier 3

4. SIMPLE CASE APPLICATION  Depression 4

5. SIMPLE CASE APPLICATION  Fruits Example: P(Banana) = 500 / 1000 = 0,5 1-0,5 = 0,5 (Not banana) New fruit  compute all the chances 5 Sweet Not sweet Total Banana350150500 Orange150 300 Other fruit15050200 Total6503501000

6. REAL-LIFE APPLICATION NAIVE BAYES  Medical Data Classification with Naive Bayes Approach  Introduction  Requirements for systems dealing with medical data  An empirical comparison  Tables  Conclusion 6

7. TABLE 2:COMPARATIVE ANALYSIS BASED ON PREDICTIVE ACCURACY 7

8. TABLE 3:COMPARATIVE ANALYSIS BASED ON AREA UNDER ROC CURVE (AUC) 8

9. REAL-LIFE APPLICATION K-NN  Used to help health care professionals in diagnosing heart disease.  Useful for pattern recognition and classification.  Euclidean distance:  Often normalized data due to different variable formats. 9

10. CASE STUDY  “Our customer is a Dutch charity organization that wants to be able to classify it's supporters to donators and non-donators. The non-donators are sent a single marketing mail a year, whereas the donators receive multiple ones (up to 4).”  Who are the donators?  Who are the non-donators?  Application of K-NN & Naive Bayes to training and test dataset.  4000 customers.  SPSS, Excel, XLMiner 10

11. CLEAN-UP  No missing values  1-dimensional outliers removed through sorting (regarding annual & average donation)  2-dimensional outliers removed through scatterplot 11

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13. Variables Kept Average donation Frequency of Response Median Time of Response Time as client Variables removed Annual donation Last donation Time since last response. 13

14.  Normalization of scores into z-scores.  Nominal categorization of data  Classification through percentiles of z-score & by manually processing values within the variables. 14

15. ANALYSIS OF CASE STUDY – K-NN 15  Xlminer  Partition data  Models created:  M1 = Zavgdon & Zfrqres  M2 = ZtimeCl, Zfrqres & Zavgdon  M3 = Zmedtor, Zfrqres & Zavgdon  ZtimeCl, Zfrqres, Zmedtor & Zavgdon

16.  Validation Data Scoring - Summary Report (for k = 13) 16 Error Report Class# Cases# Errors% Error 0 108318016,62049861 1 53626048,50746269 Overall 161944027,17726992 Classification Confusion Matrix Predicted Class Actual Class01 0 903180 1 260276

17. CHOOSING MODEL FOR K-NN  Accuracy: Proportion of correctly classified instances.  Error rate: (1 – Accuracy)  Sensitivity: Sensitivity is the proportion of actual positives which are correctly identified as positives by the classifier.  Specificity: Like sensitivity, but for the negatives. 17

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19. M1M2 Selecting everyone in validation data €711.20€662.80 Selecting while correcting for sensitivity and specificity €583.60€530.80 19

20. APPLICATION OF MODEL ON TEST DATA Classification Confusion Matrix Predicted Class Actual Class01 0 2300344 1 654750 20 Error Report Class# Cases# Errors% Error 0 264434413,01059 1 140465446,5812 Overall 404899824,65415

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22. ANALYSIS OF THE CASE STUDY – NAIVE BAYES 22 Classification Confusion Matrix Predicted Class Actual Class01 0 856229 1 225309 Error Report Class# Cases# Errors% Error 0 108522921,10599 1 53422542,13483 Overall 161945428,042  M1 = Cfrqres & Cavgdon  M2 = Cfrqresp, Cavgdon & Cmedtor Classes --> Input Variab les 01 ValueProbValueProb CFRQR ES 10,7197710,297483 20,17146520,255149 30,0639830,19222 40,04478640,255149 CAVGD ON 10,63275810,297483 20,27255320,471396 30,07613630,16476 40,01855440,066362

23. 23 Model 1Model 2 Selecting everyone€1072€1006 Selecting by class€2460,82€2378.01

24. APPLICATION OF MODEL ON TEST DATA Classes --> Input Variabl es 01 ValueProbValueProb CFRQR ES 10,71450210,306108 20,17333820,254261 30,06646530,18608 40,04569540,253551 CAVGD ON 10,63028710,3125 20,28058920,461648 30,06835330,168324 40,0207740,057528 24

25. 25 Classification Confusion Matrix Predicted Class Actual Class01 0 2096548 1 570834 Error Report Class# Cases# Errors% Error 0 264454820,72617 1 140457040,59829 Overall 4048111827,61858

26. LOOKING AT BOTH MODELS 26

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28. QUESTIONS? 28