What we got from the Predicting Red Hat Business Value competition
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This slide is to share what I've learned from the kaggle competition. There 3 topics -1) Overview of the competition 2) Introduction to Decision Tree and 3) R package XGboost.
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What we got from the Predicting Red Hat Business Value competition
- 1. What we got from Red Hat competition By Umaporn Kerdsaeng DSTO Knowledge Sharing : 10/27/2016
- 2. Topics: 1. Overview: Red Hat Competition 2. Introduction to Decision Tree 3. R package for Decision Tree (xgboost)
- 3. Overview: Red Hat Competition
- 5. What is ROC? • ROC : receiver operating characteristic • The ROC curve was first developed by electrical engineers and radar engineers during World War II for detecting enemy objects in battlefields. • ROC curve is a graphical plot that illustrates the performance of a binary classifier system as its discrimination threshold is varied. • The curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. https://en.wikipedia.org/wiki/Receiver_operating_characteristic
- 6. Sensitivity and Specificity https://www.youtube.com/watch?v=Z5TtopYX1Gc • True Positive (tp) – Detection • False Positive (fp) – False alarm • True Negative (tn) • False Negative (fn) • Sensitivity = Probability of Detection • Specificity = Probability of True Negative • 1-Specificity = Probability of False alarm
- 7. Actual outcome distribution AUC = 0.991725
- 8. receiver operating characteristic (ROC) https://www.youtube.com/watch?v=gYIlKUP2hk0 the ROC curve can be generated by plotting the cumulative distribution function of the detection probability in the y-axis versus the cumulative distribution function of the false- alarm probability in x-axis.
- 11. Red Hat Data:
- 12. Introduction to Decision Tree
- 13. Introduction to Decision Tree: https://www.youtube.com/watch?v=eKD5gxPPeY0
- 14. Introduction to Decision Tree: https://www.youtube.com/watch?v=eKD5gxPPeY0
- 15. Introduction to Decision Tree: https://www.youtube.com/watch?v=eKD5gxPPeY0
- 16. Introduction to Decision Tree: https://www.youtube.com/watch?v=eKD5gxPPeY0
- 17. Introduction to Decision Tree: https://www.youtube.com/watch?v=eKD5gxPPeY0
- 18. Introduction to Decision Tree: https://www.youtube.com/watch?v=eKD5gxPPeY0
- 19. Introduction to Decision Tree: https://www.youtube.com/watch?v=AmCV4g7_-QM
- 20. Introduction to Decision Tree: https://www.youtube.com/watch?v=AmCV4g7_-QM
- 21. Introduction to Decision Tree: https://www.youtube.com/watch?v=nodQ2s0CUbI
- 22. Introduction to Decision Tree: https://www.youtube.com/watch?v=AmCV4g7_-QM Count All 14 5 4 5 Count Yes 9 2 4 3 Count No 5 3 0 2 P+ 0.64 0.40 1 0.60 P- 0.36 0.60 0 0.40 (P+)(log(P+,2) -0.41 -0.53 0 -0.44 (P-)(log(P-,2) -0.53 -0.44 #NUM! -0.53 H(S) 0.94 0.97 0 0.97 weigth 0.36 0.29 0.36 0.94 0.35 0.00 0.35 Gain(S,wind) 0.25 Count All 14 8 6 Count Yes 9 6 3 Count No 5 2 3 P+ 0.64 0.75 0.50 P- 0.36 0.25 0.50 (P+)(log(P+,2) -0.41 -0.31 -0.50 (P-)(log(P-,2) -0.53 -0.50 -0.50 H(S) 0.94 0.81 1.00 weigth 0.57 0.43 0.94 0.46 0.43 Gain(S,wind) 0.0481
- 24. A Visual Introduction to Machine Learning http://www.r2d3.us/visual-intro-to-machine-learning-part-1/
- 25. R package for Decision Tree (xgboost)
- 26. XGBoost: Extreme Gradient Boosting • An optimized distributed gradient boosting library • XGBoost only works with numeric vectors. you need to convert all other forms of data into numeric vectors. • XGBoost provides a convenient function to do cross (an important method to measure the model’s prediction power). • XGBoost can handle missing values in the data
- 27. XGBoost: Extreme Gradient Boosting https://www.youtube.com/watch?v=ufHo8vbk6g4 http://blog.nycdatascience.com/faculty/kaggle-winning-solution-xgboost-algorithm-let-us-learn-from-its-author-3/ The minimum information we need to provide is
- 28. XGBoost: Extreme Gradient Boosting • Step 1 Load all the libraries • Step 2 Load the dataset • Step 4 Tune and Run the model • Step 3 Data Cleaning & Feature Engineering • Step 5 Score the Test Population https://www.analyticsvidhya.com/blog/2016/01/xgboost-algorithm-easy-steps/
- 29. จิปาถะ • เรียนรู้เรื่องเดิมๆ ซ้ำๆ รอบหลังๆ จะเข้ำใจมำกขึ้น • English Knowledge Source • ไอเดียจะมำแบบไม่เป็นระเบียบ แต่เรำต้องจัดระเบียบควำมคิดและกำรทำงำน • ลองผิดลองถูกและเรียนรู้ไปพร้อมๆ กัน ต้องลงมือทำ • จดทุกอย่ำงที่ทำ (พำยเรือวนในอ่ำง)
Editor's Notes
- Which customers have the most potential business value Prediction model Classification algorithm Data: Characteristics (People) Activities (act_train, act_test)
- True Positive (tp) – สัญญาณกันขโมยดัง เมือมีขโมยมาขโมยรถ False Positive (fp) – หมาฉี่ มอไซด์ผ่าน สัญญาณกันขโมยก็ดังแล้ว – คนขี้ระแวง (Type 1 error) True Negative (tn) –เหตุการณ์ทั่วไปไม่มีอะไรเกิดขึ้น สัญญาณกันขโมยไม่ดัง False Negative (fn) – ขโมยมาขโมยรถแล้วแต่สัญญาณกันขโมยไม่ดัง – คนชะล่าใจ - เสียหายแท้จริง (Type 2 error)