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Topics in Business Intelligence

Topics in Business Intelligence. K-NN & Naive Bayes – GROUP 1. Isabel van der Lijke Nathan Bok Gökhan Korkmaz. Introduction K- nn. k-NN Classifier (Categorical Outcome) Determining Neighbors Classification Rule Example: Riding Mowers Choosing k Setting the Cutoff Value

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Topics in Business Intelligence

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  1. Topics in Business Intelligence K-NN & Naive Bayes – GROUP 1 Isabel van der Lijke Nathan BokGökhan 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

  3. IntroductionNaiveBayes • Basic Classification Procedure • Cutoff Probability Method • Conditional Probability • Naive Bayes • Advantages and shortcomings of the naive Bayes classifier

  4. Simple Case Application • Depression

  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

  6. Real-Life applicationNaiveBayes • Medical Data Classification with Naive Bayes Approach • Introduction • Requirements for systems dealing with medical data • An empirical comparison • Tables • Conclusion

  7. TABLE 2:COMPARATIVE ANALYSIS BASED ON PREDICTIVE ACCURACY

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

  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.

  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

  11. Clean-up • No missing values • 1-dimensional outliers removed through sorting (regarding annual & average donation) • 2-dimensional outliers removed through scatterplot

  12. Variables Kept Average donation Frequency of Response Median Time of Response Time as client Variables removed Annual donationLast donationTime since last response.

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

  14. Analysis of Case Study – K-NN • Xlminer Partition data • Models created: • M1 = Zavgdon & Zfrqres • M2 = ZtimeCl, Zfrqres & Zavgdon • M3 = Zmedtor, Zfrqres & Zavgdon • ZtimeCl, Zfrqres, Zmedtor & Zavgdon

  15. Validation Data Scoring - Summary Report (for k = 13)

  16. 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. Application of model on Test data

  18. Analysis of the case study – NaiveBayes • M1 = Cfrqres & Cavgdon • M2 = Cfrqresp, Cavgdon & Cmedtor

  19. Application of Model on Test Data

  20. Looking at bothmodels

  21. Questions?

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