Exploratory: ランダムフォレストの紹介

データサイエンティストの間でよく使われている機械学習のアルゴリズムのランダムフォレストの紹介と、Exploratoryの中での使い方の紹介をします。

Transcript

1. EXPLORATORY
2. 2 εϐʔΧʔ ੢ా צҰ࿠ CEO EXPLORATORY ུྺ 2016೥ɺσʔλαΠΤϯεͷຽओԽͷͨΊɺExploratory, Inc Λཱͪ ্͛Δɻ Exploratory, Inc.ͰCEOΛ຿ΊΔ͔ͨΘΒɺσʔλαΠΤϯεɾϒʔ τΩϟϯϓɾτϨʔχϯάͳͲΛ௨ͯ͠γϦίϯόϨʔͰߦΘΕ͍ͯ Δ࠷ઌ୺ͷσʔλαΠΤϯεͷීٴͱڭҭʹऔΓ૊Ήɻ ถΦϥΫϧຊࣾͰɺ16೥ʹΘͨΓσʔλαΠΤϯεͷ։ൃνʔϜΛ཰ ͍ɺػցֶशɺϏοάɾσʔλɺϏδωεɾΠϯςϦδΣϯεɺσʔ λϕʔεʹؔ͢Δ਺ଟ͘ͷ੡඼ΛੈʹૹΓग़ͨ͠ɻ @KanAugust
3. Vision ΑΓΑ͍ҙࢥܾఆΛ͢ΔͨΊʹ σʔλΛ࢖͏͜ͱ͕౰ͨΓલʹͳΔ
4. Mission σʔλαΠΤϯεͷຽओԽ
5. 5 ୈ̏ͷ೾ σʔλαΠΤϯεɺAIɺػցֶश͸౷ܭֶऀɺ։ൃऀͷͨΊ͚ͩͷ΋ͷͰ͸͋Γ·ͤΜɻ σʔλʹڵຯͷ͋ΔਓͳΒ୭΋͕ੈքͰ࠷ઌ୺ͷΞϧΰϦζϜΛ࢖ͬͯ ϏδωεσʔλΛ؆୯ʹ෼ੳͰ͖Δ΂͖Ͱ͢ɻ Exploratory͕ͦ͏ͨ͠ੈքΛՄೳʹ͠·͢ɻ
6. ୈ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
7. 質問 ExploratoryͰ؆୯ʹͰ͖ΔλεΫ 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 UI
8. EXPLORATORY ΦϯϥΠϯɾηϛφʔ
9. Analytics ϥϯμϜϑΥϨετ
10. 10 ͦͷલʹɻɻɻ
11. 11 σʔλ෼ੳͱ͸ ૬ؔɺύλʔϯΛݟ͚ͭΔ͜ͱ
12. 12 څྉ ೥ྸ ৬छ ۈଓ೥਺ ੑผ 10,000 60 Manager 24 Male 3,000 40 Sales Rep 3 Female 11,000 50 Research Director 35 Female 4,000 20 HR Rep 4 Male 5,000 30 HR Rep 5 Female 10,000 45 Manager 20 Female ஌Γ͍ͨ͜ͱ ଐੑσʔλ
13. 13 ՄࢹԽʂ
14. څྉ vs. ৬छ
15. څྉ vs. ۈଓ೥਺
16. څྉ vs. ֊ڃ
17. 17 σʔλ ૬ؔɾ ύλʔϯ ՄࢹԽͯ͠૬ؔɾύλʔϯΛҰͭҰͭ໨Ͱݟͯݕ஌͢Δ
18. 18 ΊΜͲ͍͘͞ʂ
19. 19 ΞφϦςΟΫεʂ
20. 20 σʔλ ૬ؔɾ ύλʔϯ ػցֶशɾ౷ܭ ΞφϦςΟΫεΛ࢖ͬͯ૬ؔɾύλʔϯΛޮՌతʹݟ͚ͭΔɻ ΞφϦςΟΫε
21. 21 ϥϯμϜ ϑΥϨετ Ϟσϧ ༧ଌϞσϧΛ࡞Δ σʔλ ΞϧΰϦζϜ
22. 22 Monthly Income Age Job Role Department Gender ? 60 Manager Sales Male ? 40 Sales Rep R&D Female ? 30 Research Director HR Female Monthly Income Age Job Role Department Gender 10,000 60 Manager HR Male 11,000 40 Research Director R&D Female 4,000 30 HR Rep HR Female ༧ଌ͢Δ ౴͑ͷͳ͍σʔλ Ϟσϧ ϥϯμϜ ϑΥϨετ
23. 23 Ϟσϧ͸σʔλͷதʹ͋ΔύλʔϯΛ΋ͱʹ࡞ΒΕΔ Ϟσϧ ϥϯμϜ ϑΥϨετ
24. 24 Ͳͷม਺͕ΑΓ૬͕ؔ͋Δͷ͔ɺͲ͏͍͏ؔ܎ੑ Λ͍࣋ͬͯΔͷ͔Λ஌͍ͬͯΔɻ Ϟσϧ ϥϯμϜ ϑΥϨετ
25. 25 σʔλ ΞφϦςΟΫεʹΑͬͯಘΒΕͨΠϯαΠτΛ ՄࢹԽ͢Δ͜ͱͰɺ௚ײతʹཧղ͢Δ ΞφϦςΟΫε ʢػցֶशɺ౷ܭʣ ૬ؔ / ύλʔϯ
26. 26 ϥϯμϜϑΥϨετ
27. 27 ϑΥϨετʢ৿ʣ
28. 28 Ξϯαϯϒϧֶश • ෳ਺ͷϞσϧʹֶशͤ͞ɺͦΕͧΕͷ༧ଌ݁ՌΛҰ ͭͷ༧ଌʹ·ͱΊΔɻ • ྫɿRandom Forest, XGBoost
29. 29 σʔλ ݁Ռ ܾఆ໦
30. 30 ܾఆ໦ σʔλ αϯϓϧ αϯϓϧ αϯϓϧ ݁Ռ ݁Ռ ݁Ռ ଟ਺ܾ … ϥϯμϜαϯϓϧ ϥϯμϜϑΥϨετ
31. 31 Mother Age Father Age Weight Plurality State Is Premature 40 42 5.5 1 CA TRUE 33 33 6.7 1 NY FALSE 32 36 7.0 1 WA FALSE 28 28 4.5 2 NC TRUE 24 26 6.0 1 MI FALSE 28 26 6.7 1 AZ FALSE 43 40 7.6 1 TX FALSE 38 33 4.2 2 FL TRUE 34 32 5.7 1 CA FALSE 29 33 5.2 1 NY TRUE ݩσʔλ
32. 32 Mother Age Father Age Weight Plurality State is_premature 40 42 5.5 1 CA TRUE 33 33 6.7 1 NY FALSE 32 36 7.0 1 WA FALSE 28 28 4.5 2 NC TRUE 24 26 6.0 1 MI FALSE 28 26 6.7 1 AZ FALSE 43 40 7.6 1 TX FALSE 38 33 4.2 2 FL TRUE 34 32 5.7 1 CA FALSE 29 33 5.2 1 NY TRUE ༧ଌର৅ͷྻ: is_premature (ૣ࢈͔Ͳ͏͔)
33. 33 Mother Age Weight is_premature 40 5.5 TRUE 33 6.7 FALSE 32 7.0 FALSE 28 4.5 TRUE σʔλ ߦͱྻΛϥϯμϜʹαϯϓϧ͢Δɻis_premature ྻ͸༧ଌର৅ͳͷͰͲͷαϯϓϧʹ΋ඞͣೖ Δɻ Mother Age Plurality State is_premature 28 1 AZ FALSE 43 1 TX FALSE 38 2 FL TRUE Father Age State is_premature 33 FL TRUE 32 CA FALSE 33 NY TRUE
34. 34 Mother Age Weight is_premature 40 5.5 TRUE 33 6.7 FALSE 32 7.0 FALSE 28 4.5 TRUE Mother Age Plurality State is_premature 28 1 AZ FALSE 43 1 TX FALSE 38 2 FL TRUE Father Age State is_premature 33 FL TRUE 32 CA FALSE 33 NY TRUE σʔλ αϯϓϧ͞Εͨσʔλ͔ΒϞσϧΛ࡞੒͢Δɻ
35. 35 σʔλ αϯϓϧ αϯϓϧ αϯϓϧ ݁Ռ ݁Ռ ݁Ռ ଟ਺ܾ …
36. 36 ม਺ॏཁ౓ (Variable Importance)
37. 37 • RandomForestͰ࡞ͬͨ༧ଌϞσϧ͔ΒɺͲͷม਺͕༧ଌͷࡍʹॏཁ ͳͷ͔ͱ͍͏৘ใ͕ͱΕΔɻ • ୳ࡧతσʔλ෼ੳʹΑ͘࢖ΘΕΔɻ
38. Let's Try!
39. 39 ैۀһ͕ࣙΊΔͷʹԿ͕ؔ܎͍ͯ͠Δͷ͔ௐ΂͍ͨɻ
40. 40 ΞφϦςΟΫεɾϏϡʔ
41. 41 ༧ଌର৅ྻͷબ୒
42. 42 ม਺ͷྻͷબ୒
43. 43 શͯͷྻΛ༧ଌม਺ͱͯ͠બ୒
45. 45 ༧ଌӨڹ౓
47. 47 • ॏཁ౓্Ґͷม਺ʹ͍ͭͯɺPartial Dependencyͱݺ͹ ΕΔ৘ใΛՄࢹԽ͍ͯ͠Δɻ • ಛఆͷม਺ͷΈΛಈ͔͠ɺ࢒Γͷม਺Λݻఆͨ͠ͱ͖ʹɺ ݁ՌʹͲͷΑ͏ͳӨڹ͕͋Δ͔Λɺ࡞੒ͨ͠ϞσϧΛ΋ͱ ʹϓϩοτͨ͠΋ͷɻ ม਺ॏཁ౓ - ༧ଌӨڹ౓
48. 48 ϞσϧͷαϚϦ
50. 50 ༧ଌϚτϦοΫε
51. 51 ༧ଌϚτϦοΫεϏϡʔ͸ ༧ଌ݁Ռͷਖ਼ղɺෆਖ਼ղΛ ύʔηϯτͰදࣔ͢Δɻ
52. 52 Ϟσϧͷ༧ଌਫ਼౓Λ ධՁ͢ΔͨΊͷࢦඪ
53. 53 Ϟσϧͷ༧ଌਫ਼౓ͱ͸ʁ ͲΕ͚࣮ͩࡍͷσʔλʹϚον͍ͯ͠Δ͔
54. 54 ਖ਼ղ཰ʢAccuracy RateʣΛௐ΂Δ
55. 55 TRUE FALSE TRUE 5 15 FALSE 15 195 ༧ଌͷ݁Ռ ࣮ࡍͷσʔλ
56. 56 TRUE FALSE TRUE 5 15 FALSE 15 195 ࣮ࡍͷ݁Ռ ਖ਼ղ཰ = (5 + 195) / 240 = 0.875 ༧ଌͷ݁Ռ
57. 57 ਖ਼ղ཰͑͞Α͚Ε͹ͦΕͰ͍͍ͷ͔ʁ
58. 58 TRUE FALSE TRUE True Positive λΠϓ2 Τϥʔ (ୈೋछաޡ) FALSE λΠϓ1 Τϥʔ (ୈҰछաޡ) True Negative ࣮ࡍͷ஋ ༧ଌͷ݁Ռ
59. 59 ͋ͳͨ೛৷ ͯ͠·͢Ͷ ͋ͳͨ೛৷ ͯ͠·ͤΜΑ
60. 60 TRUE FALSE TRUE 5 15 FALSE 15 200 ࣮ࡍͷ஋ Precisionʢద߹཰ʣ Precision = 5 / (5+15) = 25% ༧ଌͷ݁Ռ
61. 61 TRUE FALSE TRUE 5 15 FALSE 15 200 ࣮ࡍͷ஋ TRUEͱ༧ଌ͞Ε͕ͨɺͦΕ͕ਖ਼͍͠ͷ͸25%͚ͩͩͬͨɻ (5 / (5+15)). Precision = 5 / (5+15) = 25% ༧ଌͷ݁Ռ
62. 62 TRUE FALSE TRUE 5 15 FALSE 15 200 ࣮ࡍͷ஋ TRUEͱ༧ଌ͞Ε͕ͨɺͦΕ͕ਖ਼͍͠ͷ͸25%͚ͩͩͬͨɻ (5 / (5+15)). Precision = 5 / (5+15) = 25% ༧ଌͷ݁Ռ λΠϓ̍Τϥʔ͕75%΋ى͖͍ͯΔ
63. 63 ΋͠TRUEͱ༧ଌ͢ΔͳΒɺ݁Ռ͕ຊ౰ʹ TRUEͰ͋Δ΂͖ɻ λΠϓ1 Τϥʔ (ୈҰछաޡ) ͋ͳͨ೛৷ ͯ͠·͢Ͷ
64. 64 Hi Grandma <grandma_nishida@gmail.com> Kan΁ ࠓͲ͜ʹ͍·͔͢ʁେৎ෉Ͱ͔͢ʁ௕͍ؒ͋ͳ͔ͨΒͷฦ৴͕ͳ͍ ͷͰ৺഑Ͱ͢ɻԿ΋ͳ͍ͱ͍͍Μ͚ͩͲɻޙͰɺ΋͏Ұ౓࿈བྷͯ͘͠ ͍ͩ͞ɻ ͓͹͋ͪΌΜΑΓ εύϜ ΋͠TRUEͱ༧ଌ͢ΔͳΒɺ
 ݁Ռ͕ຊ౰ʹTRUEͰ͋Δ΂͖ɻ λΠϓ1 Τϥʔ (ୈҰछաޡ)
65. 65 TRUE FALSE TRUE 5 15 FALSE 15 200 ࣮ࡍͷ஋ Recallʢݕग़཰ʣ Recall = 5 / (5+15) = 25% ༧ଌͷ݁Ռ
66. 66 TRUE FALSE TRUE 5 15 FALSE 15 200 ࣮ࡍͷ஋ ࣮ࡍʹTRUEͷͱ͖ʹɺͦΕΛ༧ଌग़དྷͨͷ͸25%͚ͩͩͬͨɻ Recall = 5 / (5+15) = 25% ༧ଌͷ݁Ռ
67. 67 TRUE FALSE TRUE 5 15 FALSE 15 200 ࣮ࡍͷ஋ ࣮ࡍʹTRUEͷͱ͖ʹɺͦΕΛ༧ଌग़དྷͨͷ͸25%͚ͩͩͬͨɻ Recall = 5 / (5+15) = 25% ༧ଌͷ݁Ռ λΠϓ̎Τϥʔ͕75%΋ى͖͍ͯΔ
68. 68 FALSEͱ༧ଌ͢ΔΑΓ͸TRUEͱ༧ଌ ͢Δํ͕·͠ɻ TRUEͷ࣌ʹɺͦΕΛTRUEͱ༧ଌ͠ ͳ͔ͬͨ৔߹ɺॏେͳଛ֐ΛҾ͖ى͜ ͢Մೳੑ͕͋Δɻ λΠϓ2 Τϥʔ (ୈೋछաޡ) ͋ͳͨ೛৷ ͯ͠·ͤΜ
69. 69 F Score: 0͔Β1ͷؒͷ஋ΛͱΓɺ1ʹ͍ۙ΄Ͳྑ͍ɻ F Score RecallͱPrecisionͷฏۉʢHarmonic Mean) 0.25
70. 70 ภͬͨ༧ଌ݁Ռ ༧ଌϚτϦοΫεΛݟΔͱɺϞσϧ͸ ୯ʹ΄ͱΜͲ͍ͭͰ΋FALSEͱ༧ଌͯ͠ ͍Δ͜ͱ͕෼͔Δɻ
71. 71 ෆۉߧͳσʔλ ࣙΊͨैۀһͷׂ߹͸ѹ౗తʹগͳ͍ɻ
72. 72 ෆۉߧͳσʔλʹΑΔ༧ଌͷ໰୊ • FALSEͷσʔλ͕ଟ͍ͨΊɺͦͷΑ͏ͳ ଟ਺೿ͷ஋Λ౴͑ͱ͢Δ͜ͱͰਖ਼ղ͢Δ ֬཰Λ࠷େʹ͍ͯ͠ΔΑ͏ͩɻ • λΠϓ2 Τϥʔ͕ଟ͍Ϟσϧʹͳͬͯ͠ ·͍ͬͯΔɻ
73. 73 ͜ΕΒͷ໰୊͸ɺαϚϦɾϏϡʔͷࢦඪʹ΋ݱΕΔɻ Recall ͸࣮ࡍTRUEͷ৔߹ʹɺTRUEͩͱ༧ଌग़དྷׂͨ߹ɻ
 ͜ͷࢦඪ͕௿͍ͱ͖͸ɺλΠϓ2 Τϥʔ͕ଟ͘ى͖Δ͜ͱ Λࣔ͢ɻ
74. 74 σʔλͷෆۉߧΛղফ͢Δ
75. 75 ଟ਺೿σʔλΛαϯϓϦϯάͯ͠ݮΒ͠ʢΞϯμʔɾα ϯϓϦϯάʣɺগ਺೿σʔλΛ߹੒ͯ͠૿΍͢ʢΦʔ όʔɾαϯϓϦϯάʣɻ ΦʔόʔɾαϯϓϦϯά ΞϯμʔɾαϯϓϦϯά গ਺೿σʔλΛ ߹੒ͯ͠૿΍͢ গ਺೿σʔλΛ αϯϓϦϯάͯ͠ ݮΒ͢ ݩσʔλ ݩσʔλ
76. 76 • গ਺೿σʔλΛ߹੒ͯ͠૿΍͢ʢΦʔόʔɾαϯϓϦϯάʣͷ ͨΊʹɺSMOTE (Synthetic Minority Oversampling Technique) ͱ ͍͏ΞϧΰϦζϜ͕࢖͑Δɻ • ExploratoryͰ͸ɺΞφϦςΟΫεɾϏϡʔͷதͷϓϩύςΟ͔ Βઃఆ͢Δ͜ͱ͕Ͱ͖Δɻ • ·ͨɺσʔλϥϯάϦϯάͷεςοϓͱͯ͠ߦ͏͜ͱ΋Ͱ͖ Δɻ SMOTEʹΑΔগ਺೿σʔλͷ߹੒
77. 77 SMOTE • গ਺೿σʔλΛϥϯμϜʹબͿ • બ͹Εͨগ਺೿σʔλʹ࠷΋͍ۙɺଞͷগ਺೿σʔ λΛݟ͚ͭΔɻ • 2ͭͷσʔλͷதؒ఺ΛϥϯμϜʹબͼɺ2ͭͷσʔ λ͔Βͷ஋Λൺྫ഑෼ͨ͠஋ΛׂΓ౰ͯΔɻ
78. YesΛબΜͰσʔλͷෆۉߧΛղফ ͢ΔͨΊͷલॲཧΛ͢Δɻ
79. • Ϟσϧ͕TRUEͱ༧ଌ͢Δׂ߹͕૿ ͍͑ͯΔɻ • ౴͕͑TRUEͰ͋Δ৔߹Ͱ΋͔ͬ͠ Γͱ༧ଌͰ͖͍ͯΔɻ
80. RecallͱɺRecallͱPrecisionͷฏۉͰ͋Δ F Score͕վળ͞Ε͍ͯΔɻ
81. ઌ΄Ͳͱ͸ɺएׯɺॱҐ͕มΘ͍ͬͯΔɻ
82. TRUEͷ֬཰্͕͕͍ͬͯΔɻ
83. 83 ෆۉߧΛௐ੔͢Δલ
84. 84 ม਺ॏཁ౓ͷ໰୊
85. • ݁ہɺͲͷม਺͕Өڹ͋Δͷ͔͕Θ͔Βͳ͍ɻ • ϥϯμϜϑΥϨετͷʮϥϯμϜੑʯʹΑͬͯɺྲྀͨ͢ͼʹม਺ॏཁ ౓ͷॱҐ͕ҧͬͯ͘Δɻ 85
86. 86 Ͳͷม਺͕ॏཁͳͷ͔?
87. 87 ܾఆ໦ σʔλ αϯϓϧ αϯϓϧ αϯϓϧ ౤ථ ౤ථ ౤ථ ݁࿦ … ϥϯμϜαϯϓϧ ϥϯμϜαϯϓϧΛར༻͍ͯ͠ΔͷͰɺ݁ՌʹϥϯμϜੑ͕͋Δɻ
88. 88 • ݁ՌʹϥϯμϜੑ͕͋ΔͷͳΒɺॏཁͩͱ͞Εͨม਺΋ۮ વࠓճͦ͏͍͏݁Ռʹͳ͚ͬͨͩͰ͸ʁ • ม਺ͷॏཁ౓͕ۮવͳͷ͔ɺ౷ܭతʹݕఆͯ͠ΈΕ͹Α ͍ɻ Ϙϧʔλ
89. 89 ϘϧʔλΛ༗ޮԽ
91. 91 20ճϥϯμϜϑΥϨετͷϞσϧΛ࡞ͬͨͱ ͖ͷ݁Ռͷ஋ͷ෼෍Λശͻ͛ਤͰද͢ɻ
92. 92 ͦΕͧΕͷม਺͕༧ଌͷ໾ʹͨͭͷ͔Ͳ͏ ͔ͷ౷ܭతͳ൑அΛ৭Ͱද͢ɻ
93. 93 ༧ଌͷ໾ʹཱͭͱ౷ܭత ʹ൑அ͞Εͨม਺
94. 94 ༧ଌͷ໾ʹͨͭͷ͔Ͳ͏͔
 ·ͩ൑அग़དྷ͍ͯͳ͍ม਺
95. 95 ༧ଌͷ໾ʹཱͨͳ͍ͱ౷ܭత ʹ൑அ͞Εͨม਺
96. ૬ؔͷՄࢹԽ 96
97. 97 σʔλ ΞφϦςΟΫεʹΑͬͯಘΒΕͨΠϯαΠτΛ ՄࢹԽ͢Δ͜ͱͰɺ௚ײతʹཧղ͢Δ ΞφϦςΟΫε ʢػցֶशɺ౷ܭʣ ૬ؔ / ύλʔϯ
98. 98 νϟʔτΛ࢖͔ͬͯ֬ΊΔ
99. 99 ͲͷνϟʔτΛ࢖͑͹Α͍ͷ͔ʁ
100. 100 σʔλͷλΠϓͷ૊Έ߹Θͤ࣍ୈʂ
101. 101 • ΧςΰϦʔ vs. ਺஋ • ਺஋ vs. ਺஋ • ΧςΰϦʔ vs. ΧςΰϦʔ σʔλͷλΠϓͷ૊Έ߹Θͤ
102. 102 • ΧςΰϦʔ vs. ΧςΰϦʔ • ΧςΰϦʔ vs. ਺஋ • ਺஋ vs. ਺஋ ૬ؔΛՄࢹԽ͢Δ
103. 103 ΧςΰϦʔ vs. ΧςΰϦʔ ΧςΰϦʔಉ࢜ͷؔ܎ΛݟΔʹ͸ɺΧςΰϦʔͷ ૊Έ߹Θͤ͝ͱʹ਺Λ਺্͑͛Δ͔ɺͦͷશମʹ ର͢Δׂ߹Λܭࢉͦ͠ͷ݁ՌΛՄࢹԽ͢Δɻ
104. 104 ώʔτϚοϓ όϒϧ ελοΫɾόʔ
105. Overtime (Category) vs. Attrition (Category/Logical)
106. Job Level (Category) vs. Attrition (Category/Logical)
107. 107 • ΧςΰϦʔ vs. ΧςΰϦʔ • ΧςΰϦʔ vs. ਺஋ • ਺஋ vs. ਺஋ ૬ؔΛՄࢹԽ͢Δ
108. 108 ശͻ͛ਤ όΠΦϦϯਤ ີ౓ۂઢ
109. Monthly Income (Numeric) vs. Attrition (Category/Logical)
110. Monthly Income (Numeric) vs. Attrition (Category/Logical)
111. 111 • ΧςΰϦʔ vs. ΧςΰϦʔ • ΧςΰϦʔ vs. ਺஋ • ਺஋ vs. ਺஋ ૬ؔΛՄࢹԽ͢Δ
112. 112 ࢄ෍ਤ
114. • ϓϩάϥϛϯάͳ͠ RݴޠͷUIͰ͋ΔExploratoryΛ෼ੳπʔϧͱͯ͠࢖༻͢ΔͨΊडߨத͸ɺϏδωεͷ ໰୊Λղܾ͢ΔͨΊʹඞཁͳσʔλαΠΤϯεͷख๏ͷशಘʹ100ˋूதͰ͖Δ • ෼ੳπʔϧͷϕϯμʔϩοΫΠϯͳ͠ ExploratoryͰͷ࡞ۀ͸શͯಠཱͨ͠ΦʔϓϯιʔεͷR؀ڥͰ࠶ݱ͕Մೳ • ࢥߟྗͱεΩϧͷशಘ σʔλαΠΤϯεͷεΩϧशಘ͚ͩͰͳ͘ɺσʔλ෼ੳʹඞཁͳࢥߟྗ΋शಘͰ͖Δ ಛ௃
115. Q & A
116. ࿈བྷઌ ϝʔϧ kan@exploratory.io ΢ΣϒαΠτ https://ja.exploratory.io ϒʔτΩϟϯϓɾτϨʔχϯά https://ja.exploratory.io/training-jp Twitter @KanAugust