ロジスティック回帰 Part 1 - 基礎編

1. ϩδεςΟοΫճؼ Part 1 جૅฤ Exploratory Seminar #19
2. EXPLORATORY
3. 3 εϐʔΧʔ ੢ా צҰ࿠ CEO EXPLORATORY ུྺ 2016೥ɺσʔλαΠΤϯεͷຽओԽͷͨΊɺExploratory, Inc Λ ্ཱͪ͛Δɻ Exploratory, Inc.ͰCEOΛ຿ΊΔ͔ͨΘΒɺσʔλαΠΤϯεɾ ϒʔτΩϟϯϓɾτϨʔχϯάͳͲΛ௨ͯ͠γϦίϯόϨʔͰ ߦΘΕ͍ͯΔ࠷ઌ୺ͷσʔλαΠΤϯεͷීٴͱڭҭʹऔΓ૊ Ήɻ ถΦϥΫϧຊࣾͰɺ16೥ʹΘͨΓσʔλαΠΤϯεͷ։ൃνʔ ϜΛ཰͍ɺػցֶशɺϏοάɾσʔλɺϏδωεɾΠϯςϦδΣ ϯεɺσʔλϕʔεʹؔ͢Δ਺ଟ͘ͷ੡඼ΛੈʹૹΓग़ͨ͠ɻ @KanAugust
4. Vision ΑΓΑ͍ҙࢥܾఆΛ͢ΔͨΊʹ σʔλΛ࢖͏͜ͱ͕౰ͨΓલʹͳΔ
5. Mission σʔλαΠΤϯεͷຽओԽ
6. 6 ୈ̏ͷ೾ σʔλαΠΤϯεɺAIɺػցֶश͸౷ܭֶऀɺ։ൃऀͷͨΊ͚ͩͷ΋ͷͰ͸͋Γ·ͤΜɻ σʔλʹڵຯͷ͋ΔਓͳΒ୭΋͕ੈքͰ࠷ઌ୺ͷΞϧΰϦζϜΛ࢖ͬͯ ϏδωεσʔλΛ؆୯ʹ෼ੳͰ͖Δ΂͖Ͱ͢ɻ Exploratory͕ͦ͏ͨ͠ੈքΛՄೳʹ͠·͢ɻ
7. ୈ1ͷ೾ ୈ̎ͷ೾ ୈ̏ͷ೾ ϓϥΠϕʔτ(ߴ͍/ݹ͍) Φʔϓϯɾιʔε(ແྉ/࠷ઌ୺) UI & ϓϩάϥϛϯά ϓϩάϥϛϯά 2016 2000 1976 ϚωλΠθʔγϣϯ ίϞσΟςΟԽ ຽओԽ ౷ܭֶऀ σʔλαΠΤϯςΟετ Exploratory ΞϧΰϦζϜ Ϣʔβʔɾ ମݧ πʔϧ Φʔϓϯɾιʔε(ແྉ/࠷ઌ୺) UI & ࣗಈԽ ϏδωεɾϢʔβʔ ςʔϚ σʔλαΠΤϯεͷຽओԽ
8. 質問 ExploratoryɹϞμϯˍγϯϓϧ UI 伝える データアクセス データ ラングリング 可視化 アナリティクス 統計/機械学習
9. ϩδεςΟοΫճؼ Part 1 جૅฤ Exploratory Seminar #19
10. 質問 伝える データアクセス データ ラングリング 可視化 アナリティクス 統計/機械学習
11. ઢܗճؼͷ෮श
12. USͷ੺ͪΌΜσʔλ
13. ໰୊ ෕਌ͷ೥ྸ͸͍͔ͭ͘ɺ฼਌ͷ೥ྸΛ΋ͱʹ༧ଌ͍ͨ͠ɻ
14. ෕਌ͷ೥ྸ vs. ฼਌ͷ೥ྸ
15. ઢܗճؼ
16. Father_Age = 0.866 * Mother_Age + 6.28 ੾ย ܎਺ʢ܏͖ʣ
17. ڵຯͷର৅ ਺஋ ΧςΰϦʔ/ೋ߲ 17 ΧςΰϦʔ/ଟ߲
18. ڵຯͷର৅ ਺஋ ΧςΰϦʔ/ೋ߲ 18 ΧςΰϦʔ/ଟ߲
19. ͜ͷϢʔβʔ͸ίϯόʔτ͢Δ͔ʁ ͜ͷऔҾ͸ෆਖ਼͔ʁ ͜ͷैۀһ͸΍ΊΔ͔ʁ ͜ͷ੺ͪΌΜ͸ະख़ࣇͰੜ·ΕΔ͔ʁ ೋ߲ͷ࣭໰
20. 20 ਺஋ͷ໰୊Λೋ߲ͷ໰୊΁
21. 21 ੺ͪΌΜͷ೛৷ظؒ
22. 22 premature = gestation week < 37 ੜ·Εͯ͘Δ੺ͪΌΜ͸ະख़ࣇ͔ʁ TRUE FALSE
23. Numeric Binary ਺஋ͷ໰୊Λೋ߲෼ྨͷ໰୊΁
24. ໰୊ ෕਌͕35ࡀΑΓ্͔Ͳ͏͔ɺ฼਌ͷ೥ྸΛ΋ͱʹ༧ଌ͍ͨ͠ɻ
25. ճؼͷΞϧΰϦζϜΛ࢖ͬͯೋ߲ͷ໰୊ΛղܾͰ͖ͳ͍͔ʁ
26. YES YOU CAN!
27. ͔͠͠ɺͪΐͬͱ޻෉͕ඞཁͰɺੲͷਓ͸ۤ͠Μͩɻ
28. ͓͔͛Ͱɺࢲୡ͸ϩδεςΟοΫճؼͱͯ͠࢖͏͚ͩɻ
29. ͲΜͳ͜ͱ΍ͬͯΔͷ͔ͪΐͬͱ೷͍ͯΈ·͠ΐ͏ɻ
30. 1. TRUE/FALSEΛ̌/̍ͱ͍͏਺஋ʹͯ֬͠཰Λ༧ଌ͢Δ໰୊ʹ͢Δ ϩδεςΟοΫճؼͷ࢓૊Έ
31. 1. TRUE/FALSEΛ̌/̍ͱ͍͏਺஋ʹͯ֬͠཰Λ༧ଌ͢Δ໰୊ʹ͢Δ 2. ͦͷ··ͩͱ͍͔ͭ͘໰୊͕͋ΔͷͰσʔλΛՃ޻͢Δ ϩδεςΟοΫճؼͷ࢓૊Έ
32. 1. TRUE/FALSEΛ̌/̍ͱ͍͏਺஋ʹͯ֬͠཰Λ༧ଌ͢Δ໰୊ʹ͢Δ 2. ͦͷ··ͩͱ͍͔ͭ͘໰୊͕͋ΔͷͰσʔλΛՃ޻͢Δ 3. ༧ଌ͞ΕΔ֬཰Λ΋ͱʹTRUE͔FALSEͷϥϕϧ෇͚Λͯ͠Ξ΢τϓοτ͢Δ ϩδεςΟοΫճؼͷ࢓૊Έ
33. 1. TRUE/FALSEΛ̌/̍ͱ͍͏਺஋ʹͯ֬͠཰Λ༧ଌ͢Δ໰୊ʹ͢Δ 2. ͦͷ··ͩͱ͍͔ͭ͘໰୊͕͋ΔͷͰσʔλΛՃ޻͢Δ 3. ༧ଌ͞ΕΔ֬཰Λ΋ͱʹTRUE͔FALSEͷϥϕϧ෇͚Λͯ͠Ξ΢τϓοτ͢Δ 1. ֬཰ 2. Φοζ 3. ϩάɾΦοζ 4. ֬཰ ϩδεςΟοΫճؼͷ࢓૊Έ
34. Step by Step
35. 1. TRUE/FALSEΛ̌/̍ͱ͍͏਺஋ʹͯ֬͠཰Λ༧ଌ͢Δ໰୊ʹ͢Δ 2. ͦͷ··ͩͱ͍͔ͭ͘໰୊͕͋ΔͷͰσʔλΛՃ޻͢Δ 3. ༧ଌ͞ΕΔ֬཰Λ΋ͱʹTRUE͔FALSEͷϥϕϧ෇͚Λͯ͠Ξ΢τϓοτ͢Δ 1. ֬཰ 2. Φοζ 3. ϩάɾΦοζ 4. ֬཰ ϩδεςΟοΫճؼͷ࢓૊Έ
36. Binary Numeric TRUE or FALSE 1 or 0
39. ֬཰ 100% 0% ฼਌ͷ೥ྸ
40. ਺஋ͷ༧ଌͳͷͰઢܗճؼʹ͔͚ͯΈΑ͏ʂ
41. ઢܗճؼͷϞσϧ
42. P(Father > 35) = a * Mother_Age + b P(): ֬཰ΛٻΊΔؔ਺
44. Pr(Father > 35) = 0.039 * Mother_Age -0.85
45. ઢܗճؼͷϞσϧ Pr(Father > 35) = 0.039 * Mother_Age -0.85
46. ͓฼͞Μ͕35ࡀͷͱ͖ͷ֬཰͸ʁ
47. Pr(Father > 35) = 0.039 * 35 - 0.85 = 0.515 51.5% ͷ֬཰Ͱ෕਌͸35ࡀΑΓ্ɻ Pr(Father > 35) = 0.039 * Mother_Age -0.85 ฼਌ͷ೥ྸ: 35
48. 35ࡀ 51.5%
49. ͓฼͞Μ͕20ࡀͷͱ͖ͷ֬཰͸ʁ
50. Pr(Father > 35) = 0.039 * 20 - 0.85 = -0.07 Pr(Father > 35) = 0.039 * Mother_Age -0.85 ϚΠφε 7% ͷ֬཰Ͱ෕਌͸35ࡀΑΓ্ɻ ฼਌ͷ೥ྸ: 20
51. 20ࡀ -7%
52. ϚΠφεͷ֬཰ʁʁʁ
53. ϚΠφεͷ֬཰͸ڹ͖͸͓΋͠Ζ͍͕ɺ ࣮ࡍʹ͸શ͘ҙຯΛͳ͞ͳ͍ɻ
54. ͜ͷลΓʹ͘Δਓͨͪͷઆ໌͕͏·͘Ͱ͖ͳ͍ɻ
55. Ͱ΋ɺσʔλ͸͜ͷลʹ΋͔ͬ͠Γ͋ΔͷͰɺͪΌΜͱઆ໌Ͱ͖ΔϞ σϧ͕΄͍͠ɻ
56. ઢܗճؼ͸0͔Β1·Ͱͷ஋͔͠औΒͳ͍֬཰Λ༧ଌ͢ΔͨΊʹ ͸͋·Γద͍ͯ͠ͳ͍ɻ
57. ্ݶɺԼݶΛ͚ͭΔͷ͸Ͳ͏ͩʁ 0% 100%
58. ͱ͍͏͜ͱ͸ɺ฼਌͕21ࡀΑΓए͔ͬͨΒ0ˋͱ͍͏͜ͱɻ 0% 100%
59. 0% 100% ͔͠͠ɺ࣮ࡍʹ͸1(෕਌ͷ೥ྸ͕35ΑΓ্ʣͷਓୡ΋͍Δɻ
60. 0% 100% ͓͍͠ɺ΋͏Ұ޻෉ඞཁʂ
61. σʔλͷม׵ʂ
62. 1. TRUE/FALSEΛ̌/̍ͱ͍͏਺஋ʹͯ֬͠཰Λ༧ଌ͢Δ໰୊ʹ͢Δ 2. ͦͷ··ͩͱ͍͔ͭ͘໰୊͕͋ΔͷͰσʔλΛՃ޻͢Δ 3. ༧ଌ͞ΕΔ֬཰Λ΋ͱʹTRUE͔FALSEͷϥϕϧ෇͚Λͯ͠Ξ΢τϓοτ͢Δ 1. ֬཰ 2. Φοζ 3. ϩάɾΦοζ 4. ֬཰ ϩδεςΟοΫճؼͷ࢓૊Έ
63. ͲΜͳม׵͕Ͱ͖Δ͔ʁ ֬཰ͩͱ0͔Β1ͷؒͱ͍͏਺஋ͷൣғʹ੍ݶ͕͋Δͷ͕໰୊ͩɻ ճؼͷϞσϧΛ࢖͏ʹ͸࿈ଓ஍Ͱ஋ʹ੍ݶ͕ͳ͍΄͏͕͍͍ɻ [- Infinity - infinity] ੍ݶͷͳ͍࿈ଓ஋ [0 - 1] range ੍ݶͷ͋Δൣғ
64. Logit (Logistic Unit) ϩδοτؔ਺
65. Logit (Logistic Unit) Log of Odds ϩάɾΦοζ
66. 66 Φοζ(Odds) ى͖͏Δೋͭͷ݁Ռͷ֬཰ͷൺ
67. 67 Φοζ Φοζ = TRUEͷ֬཰ / FALSEͷ֬཰
68. 68 ෕਌͕35ࡀΑΓ্Ͱ͋ΔΦοζ Φοζ = TRUEͷ֬཰ / FALSEͷ֬཰ ෕਌͕35ࡀΑΓ্ͷ֬཰͕10% ෕਌͕35ࡀΑΓ্ͷ֬཰͕90% 0.1111… = 10 / 90
69. ֬཰ vs. Φοζ P(Father > 35) = 0.2 P(TRUE) = P(Father > 35) = 0.2 P(FALSE): 1 - P(Father > 35) = 0.8 Φοζ = P(TRUE) / P(FALSE) = 0.2 / 0.8 = 0.25
70. ֬཰ vs. Φοζ P(Father > 35) = 0.75 P(TRUE) = P(Father > 35) = 0.75 P(FALSE): 1 - P(Father > 35) = 0.25 Φοζ = P(TRUE) / P(FALSE) = 0.75 / 0.25 = 3
71. Pr(Father > 35) = 0 0 / (1 - 0) = 0 ֬཰ Φοζ Pr(Father > 35) = 0.5 0.5 / (1 - 0.5) = 1 Pr(Father > 35) = 0.9 0.9 / (1 - 0.9) = 9 Pr(Father > 35) = 0.999 0.999 / (1 - 0.999) = 999 Pr(Father > 35) = 1 1 / (1 - 1) = ແݶ ม׵
72. Probability can only range from 0 to 1, Odds can be 0 up to any positive number. But, we still have a problem. We want the variable that can range from any negative number to any positive number.
73. ֬཰ 0 1 Φοζ 0 1 ແݶ
74. ֬཰ 0 1 Φοζ 0 1 ແݶ ແݶ -ແݶ 0 ཧ૝
75. ϩάɾΦοζ log( P(y) 1 - P(y) ) ΦοζʹϩάΛ͔͚Δ
77. ֬཰ 0 1 Φοζ 0 1 ແݶ ແݶ -ແݶ 0 ϩάɾΦοζ log( Odds( P(y) )) Odds( P(y) )
78. ϩδοτؔ਺ log( Odds( P(y) )) = Logit(P(y)) ֬཰ΛϩάɾΦοζʹม׵ͯ͘͠ΕΔؔ਺
79. • ֬཰͸0͔Β1ͷؒͷൣғͷ஋͚ͩΛؚΉɻ • Φοζ͸0͔Βϓϥεແݶେʹଓ͘਺஋ͷൣғͷ஋͚ͩΛؚΉɻ • ϩάɾΦοζ͸ϚΠφεແݶେ͔ΒϓϥεແݶେͷؒͷͲΜͳ஋ Ͱ΋औΓ͏Δɻ
80. ϩάɾΦοζ͸ͲΜͳ਺஋Ͱ΋ͱΕΔɻ ͱ͍͏͜ͱ͸ɺ࿈ଓ਺஋Λ༧ଌ͢ΔͨΊͷճؼͷΞϧΰϦζϜ ͕࢖͑Δʂ
81. ϩδεςΟοΫճؼ खݩʹ͋Δ༧ଌม਺Λݩʹͯ͠ɺڵຯͷର৅Ͱ͋Δೋ߲ม਺ͷ ϩάɾΦοζΛ༧ଌ͢ΔͨΊͷϞσϧΛ࡞ΔͨΊͷΞϧΰϦζ Ϝɻ
82. ઢܗճؼ ϩδεςΟοΫճؼ Logit( P(Father > 35) ) = a * Mother_Age + b Father_Age = a * Mother_Age + b
83. ϩδεςΟοΫճؼΛྲྀ͠ݟͯΈΔʂ
86. Logit( Pr(Father > 35) ) = 0.029 * Mother_Age - 0.1012
87. ฼਌͕40ࡀͷ࣌ɺ෕਌͕35ࡀҎ্Ͱ͋Δ֬཰͸ʁ
88. Logit( P(Father > 35) ) = 0.29 * Mother_Age - 10.12 Logit( P(Father > 35) ) = 0.29 * 40 - 10.12 = 1.48 ෕਌͕35ࡀҎ্Ͱ͋ΔϩάɾΦοζ͸1.48ɻ ???
89. ϩάɾΦοζͷཧղ͸ࢲͷ௚ײΛ௒͍͑ͯΔɻɻɻ
90. ϩάɾΦοζΛͻͬ͘Γฦͯ֬͠཰ ʹ໭͍ͨ͠ɻ
91. 1. TRUE/FALSEΛ̌/̍ͱ͍͏਺஋ʹͯ֬͠཰Λ༧ଌ͢Δ໰୊ʹ͢Δ 2. ͦͷ··ͩͱ͍͔ͭ͘໰୊͕͋ΔͷͰσʔλΛՃ޻͢Δ 3. ༧ଌ͞ΕΔ֬཰Λ΋ͱʹTRUE͔FALSEͷϥϕϧ෇͚Λͯ͠Ξ΢τϓοτ͢Δ 1. ֬཰ 2. Φοζ 3. ϩάɾΦοζ 4. ֬཰ ϩδεςΟοΫճؼͷ࢓૊Έ
92. Logit(P(Father > 35)) = 0.03 * Mother_Age - 0.85 P(Father > 35) = Logit (0.03 * Mother_Age - 0.85) -1 ٯؔ਺
93. Logit(Pr(Father > 35)) = 0.03 * Mother_Age - 0.85 Pr(Father > 35) = (0.03 * Mother_Age - 0.85) Logistic
94. P(Father > 35) = Logistic(0.29 * Mother_Age - 10.12)
95. ฼਌͕40ࡀͷ࣌ɺ෕਌͕35ࡀҎ্Ͱ͋Δ֬཰͸ʁ
96. P(Father > 35) = Logistic(0.29 * Mother_Age - 10.12) P(Father > 35) = Logistic(0.29 * 40 - 10.12) = Logistic(1.48) = 0.8145 ฼਌͕40ࡀͷ࣌ɺ෕਌͕35ࡀҎ্Ͱ͋Δ֬཰͸81%ɻ
97. ฼਌͕20ࡀͷ࣌ɺ෕਌͕35ࡀҎ্Ͱ͋Δ֬཰͸ʁ
98. P(Father > 35) = Logistic(0.29 * Mother_Age - 10.12) P(Father > 35) = Logistic(0.29 * 20 - 10.12) = Logistic(-4.32) = 0.01312 ฼਌͕20ࡀͷ࣌ɺ෕਌͕35ࡀҎ্Ͱ͋Δ֬཰͸1.3%ɻ
99. ͜ͷϩδεςΟοΫճؼͷϞσϧ͸ͲΜͳ͔Μ͡ͷ΋ͷʁ
100. 100 ઢܗճؼͷϞσϧ vs ϩδεςΟοΫճؼͷϞσϧ
101. ઢܗճؼͷ৔߹ΛݟͯΈΔɻ
102. 102 Father_Age = a * Mother_Age + b ܎਺ʢ܏͖ʣ ੾ย ઢܗճؼͷϞσϧʢܭࢉࣜʣ
103. 103 ܎਺ʢ܏͖ʣ ੾ย
104. 104 Father_Age = 0.87 * Mother_Age + 6.28 ܎਺ʢ܏͖ʣ ੾ย ઢܗճؼͷϞσϧʢܭࢉࣜʣ
106. ෕਌ͷ೥ྸ ฼਌ͷ೥ྸ
107. ෕਌ͷ೥ྸ ฼਌ͷ೥ྸ
108. How about this Logistic Regression Model?
109. 109 ෕਌͕35Ҏ্ͷ֬཰ = logistic(a * Mother_Age + b) ܎਺ʢ܏͖ʣ ੾ย ܎਺ͱ੾ยΛௐઅ͢Δ͜ͱͰ࣮σʔλͱ Ϛον͢ΔΑ͏ͳۂઢ͕ඳ͚Δɻ
110. 110 ϩδεςΟοΫճؼͷϞσϧ
111. 111 ෕਌͕35Ҏ্ͷ֬཰ = logistic(0.29 * Mother_Age - 10.12) ੾ย ܎਺
113. ֬཰ (Father > 35) ฼਌ͷ೥ྸ
114. ϩδεςΟοΫۂઢ
115. ֬཰ (Father > 35) ฼਌ͷ೥ྸ
116. ࣮σʔλ Ϟσϧ (ϩδεςΟοΫۂઢ) ΋ͱͷσʔλͷฏۉΛऔΔͱɻɻɻ
117. ͱ͜ΖͰɺຊ౰ʹ΍Γ͔ͨͬͨͷ͸ೋ߲ͷ֬཰Ͱͳ ͯ͘ɺ஋ʢTRUE/FALSEʣͷ༧ଌɻ ฼਌ͷ೥ྸΛ΋ͱʹɺ෕਌͕̏̑ࡀҎ্ͳͷ͔Ͳ͏ ͔Λ஌Γ͍ͨɻ
118. ͱ͜ΖͰɺຊ౰ʹ΍Γ͔ͨͬͨͷ͸ೋ߲ͷ֬཰Ͱͳ ͯ͘ɺ஋ʢTRUE/FALSEʣͷ༧ଌɻ ฼਌ͷ೥ྸΛ΋ͱʹɺ෕਌͕̏̑ࡀҎ্ͳͷ͔Ͳ͏ ͔Λ஌Γ͍ͨɻ TRUE or FALSE ?
119. 1. TRUE/FALSEΛ̌/̍ͱ͍͏਺஋ʹͯ֬͠཰Λ༧ଌ͢Δ໰୊ʹ͢Δ 2. ͦͷ··ͩͱ͍͔ͭ͘໰୊͕͋ΔͷͰσʔλΛՃ޻͢Δ 3. ༧ଌ͞ΕΔ֬཰Λ΋ͱʹTRUE͔FALSEͷϥϕϧ෇͚Λͯ͠Ξ΢τϓοτ͢Δ 1. ֬཰ 2. Φοζ 3. ϩάɾΦοζ 4. ֬཰ ϩδεςΟοΫճؼͷ࢓૊Έ
120. 120 100% 0% 30 35 40 ฼਌ͷ೥ྸ ෕਌ͷ೥ྸ͕ 35ࡀҎ্Ͱ͋Δ ֬཰ ֬཰Ͱ͸ͳ͘TRUE͔FALSEʹ෼ྨ͍ͨ͠ɻ
121. 121 30 35 40 ฼਌ͷ೥ྸ TRUE FALSE ෕਌ͷ೥ྸ͕ 35ࡀҎ্ 100% 0% ֬཰Ͱ͸ͳ͘TRUE͔FALSEʹ෼ྨ͍ͨ͠ɻ
122. 122 30 35 40 ฼਌ͷ೥ྸ 50% ෕਌ͷ೥ྸ͕ 35ࡀҎ্ TRUE FALSE ͖͍͠஋Λઃ͚Δɻྫ͑͹ɺ֬཰͕50%Λڥʹ͢Δɻ
123. 123 30 35 40 ฼਌ͷ೥ྸ 50% ෕਌ͷ೥ྸ͕ 35ࡀҎ্ TRUE FALSE TRUE FALSE ͜ͷۂઢΛ࢖͏͜ͱͰ෼ྨ͢Δ͜ͱ͕Ͱ͖ΔΑ͏ʹͳͬͨɻ
124. 124 30 35 40 ฼਌ͷ೥ྸ 50% ෕਌ͷ೥ྸ͕ 35ࡀҎ্ TRUE FALSE TRUE FALSE ͜ͷۂઢΛ࢖͏͜ͱͰ෼ྨ͢Δ͜ͱ͕Ͱ͖ΔΑ͏ʹͳͬͨɻ
125. 125 30 35 40 ฼਌ͷ೥ྸ 50% ෕਌ͷ೥ྸ͕ 35ࡀҎ্ TRUE FALSE TRUE FALSE ࣮ࡍʹ͸ɺ෕਌ͷ೥ྸ͕35ࡀҎ্ͩͬͨσʔλʹ౰ͯ͸ΊͯΈΔ
126. 126 30 35 40 ฼਌ͷ೥ྸ 50% ෕਌ͷ೥ྸ͕ 35ࡀҎ্ TRUE FALSE TRUE FALSE ౰͍ͨͬͯΔ ౰͍ͨͬͯͳ͍
127. 127 30 35 40 ฼਌ͷ೥ྸ 50% ෕਌ͷ೥ྸ͕ 35ࡀҎ্ TRUE FALSE TRUE FALSE ࣮ࡍʹ͸ɺ෕਌ͷ೥ྸ͕35ࡀҎ্͡Όͳ͔ͬͨσʔλʹ౰ͯ͸ΊͯΈΔ
128. 128 30 35 40 50% ෕਌ͷ೥ྸ͕ 35ࡀҎ্ TRUE FALSE TRUE FALSE ౰͍ͨͬͯΔ ౰͍ͨͬͯͳ͍
129. 129 30 35 40 50% ෕਌ͷ೥ྸ͕ 35ࡀҎ্ TRUE FALSE TRUE FALSE ౰͍ͨͬͯͳ͍ ౰͍ͨͬͯͳ͍
130. 130 = 12/14 = 0.857 (85.7%) ͜ͷۂઢϞσϧΛ࢖͏ͱɺ 85.7%ͷਫ਼౓Ͱ༧ଌͰ͖Δɻ
131. 131 ༧ଌม਺͕1͚ͭͩͷ৔߹ΛΈ͖ͯͨɻ
132. 132 ༧ଌม਺͕ෳ਺ͷ৔߹͸ʁ
133. 133 ෕਌͕35Ҏ্ͷ֬཰ = logistic(0.3 * Mother_Age - 10) ੾ย ܎਺ ༧ଌม਺͕Mother_Age͚ͩͷ৔߹
134. 134 ෕਌͕35Ҏ্ͷ֬཰ = logistic(0.3 * Mother_Age + 1.2 * Mother_Japanese - 10) ༧ଌม਺͕Mother_AgeͱMother_Japaneseͷ ̎ͭͷ৔߹
135. Q & A
137. • ϓϩάϥϛϯάͳ͠ RݴޠͷUIͰ͋ΔExploratoryΛ෼ੳπʔϧͱͯ͠࢖༻͢ΔͨΊडߨத͸ɺϏδωεͷ໰୊ Λղܾ͢ΔͨΊʹඞཁͳσʔλαΠΤϯεͷख๏ͷशಘʹ100ˋूதͰ͖Δ • πʔϧͷ࢖͍ํͰ͸ͳ͘ɺ෼ੳख๏ͷशಘ ݱ৔Ͱ࢖͑Δ෼ੳख๏ΛάϧʔϓԋशΛ௨࣮ͯ͠ࡍʹखΛಈ͔͠ͳ͕Βɺ਎ʹ͚ͭͯߦ͘ ͜ͱ͕Ͱ͖Δɻ • ࢥߟྗͱεΩϧͷशಘ σʔλαΠΤϯεͷεΩϧशಘ͚ͩͰͳ͘ɺσʔλ෼ੳʹඞཁͳࢥߟྗ΋शಘͰ͖Δ ಛ௃
138. ࿈བྷઌ ϝʔϧ kan@exploratory.io ΢ΣϒαΠτ https://ja.exploratory.io ϒʔτΩϟϯϓɾτϨʔχϯά https://ja.exploratory.io/training-jp Twitter @KanAugust
139. EXPLORATORY