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Decision Tree Intro [의사결정나무]
- 1. 김현우 a.k.a 순록킴 yBigTa 9기 심리학 & 컴퓨터과학
- 3. the Tree Contents { decision tree, RSS, Gini, pruning };
- 6. Structure
- 10. 6 2 8 1 94 Tree as a Data Structure Binary Search tree Red-Black tree AVL tree 2,4 tree Heap
- 13. Decision Tree Regression & Classification
- 15. Predicting Baseball players’ Salary (log transformed)
- 16. Predicting Baseball players’ Salary (log transformed)
- 17. Decision Tree a top-down, greedy approach
- 22. Decision ?
- 23. How it works R1 R2 R3 R1 R3 R2
- 25. Splitting criterion, Regression RSS, Residual Sum of Squares : 잔차 제곱의 합 SSE, Sum of Squared Errors of prediction minimize
- 26. Splitting criterion, Regression RSS, Residual Sum of Squares: 잔차 제곱의 합 한 영역 안의 평균 데이터 하나 한 영역 안에서, 데이터들과 그 평균 간의 차이 의 합 모든 영역에서, 그 값들의 합들의 합 2
- 27. How it works R1 R2 R3 R1 R3 R2
- 28. Splitting criterion, Regression RSS, Residual Sum of Squares: 잔차 제곱의 합 한 영역 안의 평균 데이터 하나 한 영역 안에서, 데이터들과 그 평균 간의 차이 의 합 모든 영역에서, 그 값들의 합들의 합 2
- 29. Decision Tree, Regression RSS, Residual Sum of Squares: 잔차 제곱의 합 1. Select predictor(변수) X and cutpoint(분할 기준점) t that split the predictor space into the regions { X | X < t } and { X | X >= t } 2. Select the ones that leads to the greatest possible reduction in RSS RSS를 가장 크게 감소시키는 X와 t를 고르자 == Select the one among the resulting trees that has the lowest RSS 어떤 X와 어떤 t를 고르는지에 따라 다양한 tree시나리오가 만들어질텐데 그 중에서 RSS가 가장 작게 나오는 tree시나리오를 고르는 알고리즘
- 31. How it looks
- 33. Predicting Iris Data with 2 variables: Petal length, width
- 34. Predicting Iris Data with 2 variables: Petal length, width
- 36. Splitting Criterion Gini Index Cross Entropy
- 37. Splitting criterion, Classification Classification error rate Resubstitution error
- 38. Splitting criterion, Classification Gini index: measure of impurity 불순도 The proportion of training observations in the m-th region that are from the k-th class ‘영역 m’에서 ‘분류 k’에 해당하는 데이터의 비율 ‘영역 m’에서 ‘분류 k’에 해당하지 않는 데이터의 비율
- 39. Splitting criterion, Classification Classification error rate Resubstitution error
- 40. Splitting criterion, Classification Gini index: measure of impurity 불순도
- 41. Splitting criterion, Classification Gini index: measure of impurity 불순도 minimize
- 42. Splitting criterion, Classification Gini index: measure of impurity 불순도 ‘영역 m’에서 ‘분류 k’에 해당하지 않는 데이터의 비율 The proportion of training observations in the m-th region that are from the k-th class ‘영역 m’에서 ‘분류 k’에 해당하는 데이터의 비율
- 43. Splitting criterion, Classification Gini index: measure of impurity 불순도 가 0이나 1에 가까울수록..?
- 44. Splitting criterion, Classification Gini index: measure of impurity 불순도 weight * 전체 데이터 개수 대비 노드 안에 있는 데이터 개수의 비율
- 45. 쉬는 시간
- 46. Splitting criterion, Classification Gini index: measure of impurity 불순도 minimize
- 47. Splitting criterion, Classification Gini index: measure of impurity 불순도 직접 해봅시다
- 48. Split on Gender Split on Class X / XI
- 49. Splitting criterion, Classification Gini index: measure of impurity 불순도 Split on Gender Students: 30 Play Overwatch: 15 (50%) Students: 10 Overwatch: 2 (20%) Students: 20 Overwatch: 13 (65%) Girl Boy
- 50. Splitting criterion, Classification Gini index: measure of impurity 불순도 Split on Gender Students: 30 Play Overwatch: 15 (50%) Students: 10 Overwatch: 2 (20%) Students: 20 Overwatch: 13 (65%) ( 0.2 * 0.8 + 0.8 * 0.2 ) * 0.33 ( 0.65 * 0.35 + 0.35 * 0.65 ) * 0.66 + = 0.4 Girl Boy
- 51. Splitting criterion, Classification Gini index: measure of impurity 불순도 Split on Class Students: 30 Play Overwatch: 15 (50%) Students: 14 Overwatch: 6 (43%) Students: 16 Overwatch: 9 (56%) 10th 11th
- 52. Splitting criterion, Classification Gini index: measure of impurity 불순도 Split on Class Students: 30 Play Overwatch: 15 (50%) Students: 14 Overwatch: 6 (43%) Students: 16 Overwatch: 9 (56%) ( 0.43 * 0.57 + 0.57 * 0.43 ) * 0.47 ( 0.56 * 0.44 + 0.44 * 0.56 ) * 0.53 + = 0.49 10th 11th
- 53. Splitting criterion, Classification Gini index: measure of impurity 불순도 Split on Gender Students: 30 Play Overwatch: 15 (50%) Students: 10 Overwatch: 2 (20%) Students: 20 Overwatch: 13 (65%) Girl Boy
- 55. Stopping criterion 1. The node is pure 2. There are fewer observations than MinLeafSize 3. The algorithm splits MaxNumSplits
- 56. 작은 문제
- 57. Overfitting Algorithm becoming too specific to the data you used to train it. It cannot generalize very well to the data you haven’t given it before.
- 61. Overfit: 과적합
- 62. 가지치기
- 63. Pruning Reduced Error Pruning Cost-complexity Pruning
- 65. Tree constructing → Stop Split Split Split SplitSplit Split Split Split Split Split Split → Prune
- 67. Decision Trees CART Classification and Regression Tree C5.0 CHAID
- 69. Tree & Linearity Unlike linear models, they map non-linear relationships quite well
- 70. Tree & Linearity
- 71. Tree & Non-linearity Set of Logistic regressions
- 72. Tree & advantages 1. 이해하기 쉽다: 씹고 뜯고 맛보고 즐기고 [White box] 2. 데이터 정제가 크게 필요하지 않다: 바로 넣자 3. numerical, categorical 가리지 않는다: 그냥 넣자 4. 데이터가 어떤 패턴인지 볼 때 편하다: 넣어봐
- 73. Tree & disadvantages 1. Overfitting 2. 연속된 수치 값에는 좀 약한 모습이…
- 75. Tree 생각보다 이곳저곳 많이 쓰인다