Exploratory: An Introduction to Decision Tree

Kan will be introducing Decision Tree, which is one of the machine learning algorithms that build prediction models based on the patterns inside the data, by demonstrating it with Exploratory’s Analytics view.

1. EXPLORATORY
2. Kan Nishida co-founder/CEO Exploratory Summary Beginning of 2016, launched Exploratory, Inc. to democratize Data Science. Prior to Exploratory, Kan was a director of development at Oracle leading development teams for building various Data Science products in areas including Machine Learning, BI, Data Visualization, Mobile Analytics, Big Data, etc. While at Oracle, Kan also provided training and consulting services to help organizations transform with data. @KanAugust Instructor
3. Mission Make Data Science Available for Everyone
4. Data Science is not just for Engineers and Statisticians. Exploratory makes it possible for Everyone to do Data Science. The Third Wave
5. First Wave Second Wave Third Wave Proprietary Open Source UI & Programming Programming 2016 2000 1976 Monetization Commoditization Democratization Statisticians Data Scientists Democratization of Data Science Algorithms Experience Tools Open Source UI & Automation Business Users Theme Users
6. Analytics Decision Tree
7. Questions Communication (Dashboard, Note, Slides) Data Access Data Wrangling Visualization Analytics (Statistics / Machine Learning) Exploratory Data Analysis
8. Decision Tree Decision tree is a series of forks with conditional questions.
9. 9 Baby Weight Plurality is_Premature A 5.2 1 TRUE B 4.7 2 TRUE C 6.8 1 FALSE D 7.2 1 FALSE E 5.1 2 TRUE Z 5.8 1 ? Will this baby be prematurely born?
10. 10 Build a decision tree model to predict if a given baby is prematurely born or not.
11. 11 Visualize Weight Pound and Plurality 11 Plurality Weight Pound 1 5 2 3 4 5 6 4 7
12. 12 12 Red is Premature, Blue is Not Premature 1 5 2 3 4 5 6 4 7 Plurality Weight Pound
13. 13 13 Divide into multiple groups with same colors by drawing as less straight lines as possible. 1 5 2 3 4 5 6 4 7 Plurality Weight Pound
14. 14 14 1 5 2 3 4 5 6 4 7 Divide into 2 groups by whether Weight Pound is greater than 5.5. Plurality Weight Pound Weight Pound >= 5.5
15. 15 15 1 5 2 3 4 5 6 4 7 Divide by whether Plurality is greater than 1.5 Weight Pound >= 5.5 Pluralityʼ1.5 Plurality Weight Pound
16. 16 16 1 5 2 3 4 5 6 4 7 Weight Pound >= 5.5 Pluralityʼ1.5 Plurality Weight Pound
17. 17 17 1 5 2 3 4 5 6 4 7 Premature: No Ratio of Premature: 0% Ratio of All Babies: 40% Weight Pound >= 5.5 Pluralityʼ1.5 Plurality Weight Pound
18. 18 18 1 5 2 3 4 5 6 4 7 Premature: No Ratio of Premature: 40% Ratio of All Babies: 20% Weight Pound >= 5.5 Pluralityʼ1.5 Plurality Weight Pound
19. 19 19 1 5 2 3 4 5 6 4 7 Premature: Yes Ratio of Premature: 100%
 Ratio of All Babies: 40% Weight Pound >= 5.5 Pluralityʼ1.5 Plurality Weight Pound
20. 20 Weight Pound >= 5.5 TRUE FALSE Plurality > 1.5 TRUE FALSE 0% 40% 100% Probability of Premature
21. 21 How does it create Tree?
22. 22 Which conditions should come first?
23. 23 Gini Impurity • Ranges between 0 and 1. • A metric to measure how much different values are mixed per group. pɿPercentage of each classification result in each branch of the decision tree
24. • Which variable to use to branch the tree is determined by comparing weighted average of Gini impurity. • The lower the Gini impurity is, the more cleanly the data points are classified without branches with mixed classification results. • Variable with the smallest weighted average of Gini impurity is picked for making next branch. Gini Impurity
25. Impurity = 0 25 Not Premature Not Premature Not Premature 1 - (0/6)2 - (6/6)2 = 0 Not Premature Not Premature Not Premature
26. 26 Premature Premature Premature 1 - (6/6)2 - (0/6)2 = 0 Premature Premature Premature Impurity = 0
27. Impurity = 0.44 27 Not Premature Not Premature Not Premature Premature Premature 1 - (2/6)2 - (4/6)2 = 0.44 Not Premature
28. Impurity = 0.44 28 Not Premature Premature Premature 1 - (4/6)2 - (2/6)2 = 0.44 Not Premature Premature Premature
29. Impurity = 0.5 29 Not Premature Premature Not Premature Not Premature Premature Premature 1 - (3/6)2 - (3/6)2 = 0.5
30. 30 ૣ࢈ ૣ࢈ ૣ࢈ Impurity: 0.5 ૣ࢈ ૣ࢈ Starting Point Premature Premature Premature Premature Premature Not Premature Not Premature Not Premature Not Premature Not Premature
31. 31 Use Weight Pound to separate first
32. 32 Weight Pound >= 5.5 TRUE FALSE
33. 33 TRUE FALSE Impurity: 0 Impurity: 1- (2/7)2 - (5/7)2 = 0.41 Weight Pound >= 5.5
34. 34 TRUE FALSE Impurity: 0 Impurity: 1- (2/7)2 - (5/7)2 = 0.41 Impurity: 3/10*0 + 7/10*0.41 = 0.29 Weight Pound >= 5.5
35. 35 TRUE FALSE Impurity: 3/10*0 + 7/10*0.41 = 0.29 Impurity: 0.5 Weight Pound >= 5.5
36. 36 TRUE FALSE Impurity: 3/10*0 + 7/10*0.41 = 0.29 Impurity: 0.5 0.21 Decrease Weight Pound >= 5.5
37. 37 Use Plurality First
38. 38 Plurality > 1.5 TRUE FALSE
39. 39 TRUE FALSE Impurity: 1- (2/5)2 - (3/5)2 = 0.48 Impurity: 1- (3/5)2 - (2/5)2 = 0.48 Plurality > 1.5
40. 40 TRUE FALSE Impurity: 5/10*0.48 + 5/10*0.48 = 0.48 Impurity: 1- (2/5)2 - (3/5)2 = 0.48 Impurity: 1- (3/5)2 - (2/5)2 = 0.48 Plurality > 1.5
41. 41 TRUE FALSE Impurity: 5/10*0.48 + 5/10*0.48 = 0.48 Impurity: 0.5 Plurality > 1.5
42. 42 TRUE FALSE Impurity: 5/10*0.48 + 5/10*0.48 = 0.48 Impurity: 0.5 0.02 Decrease Plurality > 1.5
43. 43 Weight Pound helps decreasing Impurity Score better Plurality 0.02 ʻ 0.48 Weight Pound Compare Impurity Scores
44. 44 Use Weight Pound first, Then use Plurality
45. 45 TRUE FALSE TRUE FALSE 100% 50% 0% Weight Pound >= 5.5 Plurality > 1.5
46. 46 Use Plurality first, Then use Weight Pound
47. 47 Over_35 TRUE FALSE Is_Plural TRUE FALSE 50% 100% Is_Plural TRUE FALSE 100% 100% Weight Pound >= 5.5 Plurality > 1.5 Weight Pound >= 5.5
48. 48 Analytics Let’s Decision Tree!
49. Create a ‘is_premature’ Column 49 gestation_weeks < 37
50. Select ‘Mutate (Create Calculation) from the column header menu of ‘gestation_week’. 50
52. 52 Select ‘Decision Tree’
53. 53 Select ‘is_premature’ column
54. 54 Select Predictor Variables
55. 55 Select all columns except ‘gestation_weeks’
57. Starting point. The majority of data is FALSE (Not Premature). Ratio of TRUE (Premature) is 12%. Ratio of babies is 100%
58. Condition: Is weight_pounds greater than 5.3?
59. The majority of ‘Yes’ group is FALSE. Ratio of TRUE (Premature) is 8%. Ratio of babies is 94%
60. The majority of ‘No’ group is TRUE. Ratio of TRUE (Premature) is 72%. Ratio of babies is 6%