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keisu_special_lecture_20210511.pdf
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Taro Takaguchi
May 10, 2021
Technology
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keisu_special_lecture_20210511.pdf
Taro Takaguchi
May 10, 2021
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Transcript
͋Δࣄۀձࣾʹ͓͚Δ σʔλαΠΤϯεͷ࣮ ߴޱ ଠ࿕ LINEגࣜձࣾ Data Science ηϯλʔ ܭֶಛผߨٛ ౦ژେֶ
ֶ෦ ܭֶՊ 2021/05/11 1
ߴޱ ଠ࿕ʢ͔͙ͨͪ ͨΖ͏ʣ LINEגࣜձࣾ Data Science ηϯλʔ γχΞσʔλαΠΤϯςΟετ / Ϛωʔδϟʔ
~2013ɹ౦ژେֶେֶӃ ใཧֶܥݚڀՊ ཧใֶઐ߈ ɹɹɹ ത࢜՝ఔʢཧใୈ̐ݚڀࣨʣ ~2017ɹࠃཱݚڀػؔʹͯϙευΫݚڀһɹ ܦྺ ࣌ͷઐ 2 ωοτϫʔΫՊֶʢಛʹ࣌ؒతʹมԽ͢ΔωοτϫʔΫʣ
اۀʹస͖͔͚ͨͬ͡ 3 2 2 2 1 1 1 3 3
4 4 3 4 ʹཱͪͦ͏ɺͦΕͰ࣮ࣾձͱͷڑԕ͍… @ LINE DEVELOPER DAY 2019 σʔλΛ׆༻ͨ͠ࣄۀͷ࠷લઢΛ ݟ͍ͨɾؔΘΓ͍ͨ
ςʔϚ ʮ͋Δࣄۀձࣾʹॴଐ͢ΔσʔλαΠΤϯςΟετ͕ɺ ͲΜͳࣄͰษڧݚڀͷܦݧΛ׆͔͍ͯ͠Δ͔ʁʯ 4
શମͷߏ 1. ରΛΔɿ ࣄۀձࣾͷσʔλαΠΤϯςΟετͬͯͲΜͳࣄʁ ʢٳܜʣ 2. தΛΔɿ ࣮ͰΑ͘༻͍Δ౷ܭͷҰ෦ͱ۩ମతͳࣄྫ 5
1. ରΛΔɿ ࣄۀձࣾͷσʔλαΠΤϯςΟετͬͯ ͲΜͳࣄʁ 6
ͦͦσʔλαΠΤϯςΟετͱʁ 7 - اۀɾ࣌ظɾίϛϡχςΟʹΑΓఆ༷ٛʑ - ಉ͡৬໊ͰҟͳΔۀɺҟͳΔ৬໊Ͱڞ௨͢Δۀ ࢦඪΛఆٛ͠ܭଌ͢Δ / ετʔϦʔΛޠΔ /
πʔϧΛ࡞Δ Analyticsʢੳܕʣ ػցֶशͷख๏ΛɾαʔϏεʹ࣮͢Δ AlgorithmsʢΞϧΰϦζϜܕʣ ౷ܭख๏ʹΑΓҼՌؔΛཱূ͢Δ Inferenceʢਪܕʣ Ref. https://www.linkedin.com/pulse/one-data-science-job-doesnt-fit-all-elena-grewal/ Data Scientist ྨͷҰྫɿ
λεΫ͝ͱʹׂ͕͔ΕΔ 8 Q1. ɾαʔϏεʹ࣮͞ΕΔίʔυΛॻ͘ʁ Analytics ʢੳܕʣ Algorithms ʢΞϧΰϦζϜܕʣ Inference ʢਪܕʣ
Q2. ౷ܭख๏ʹΑΓҼՌؔΛݕূ͢Δʁ Yes Yes No No ྨͷҰྫɿ
৫ߏ୲ྖҬʹରԠ͍ͯ͠Δ 9 Data Science ηϯλʔ Data Science Machine Learning Machine
Learning Research Analyticsʢੳܕʣ AlgorithmsʢΞϧΰϦζϜܕʣ Inferenceʢਪܕʣ جૅݚڀ͓ΑͼࣄۀͷԠ༻ ػցֶशΤϯδχΞ
ੳɾਪܕͷ۩ମతͳࣄ ਐߦத ࣄલ ࣄޙ ࣌ظ 10 Ωϟϯϖʔϯ / ৽ػೳͷՃ /
طଘػೳͷมߋͳͲ
ੳɾਪܕͷ۩ମతͳࣄ ਐߦத ࣄલ ࣄޙ ࣌ظ 11 Ωϟϯϖʔϯ / ৽ػೳͷՃ /
طଘػೳͷมߋͳͲ - Ωϟϯϖʔϯͷ݅બఆ - ৽ػೳͷχʔζݟੵΓ - ػೳมߋͷӨڹͷݟੵΓ - etc.
ੳɾਪܕͷ۩ମతͳࣄ ਐߦத ࣄલ ࣄޙ ࣌ظ 12 Ωϟϯϖʔϯ / ৽ػೳͷՃ /
طଘػೳͷมߋͳͲ - ΦϯϥΠϯ A/B ςετ - μογϡϘʔυͷ࡞ ओཁͳࣄۀࢦඪͷϞχλϦϯάද - ҟৗͳมԽͷݕग़ - etc.
ੳɾਪܕͷ۩ମతͳࣄ ਐߦத ࣄલ ࣄޙ ࣌ظ 13 Ωϟϯϖʔϯ / ৽ػೳͷՃ /
طଘػೳͷมߋͳͲ - ࢪࡦͷޮՌݕূ - ҼՌਪ - ظతมԽͷཁҼղ - etc.
νʔϜɺϓϩδΣΫτɺϓϩμΫτ 14 νʔϜ
νʔϜɺϓϩδΣΫτɺϓϩμΫτ 15 νʔϜ ϓϩδΣΫτ
νʔϜɺϓϩδΣΫτɺϓϩμΫτ 16 νʔϜ ϓϩδΣΫτ ϓϩμΫτ ྫɿϓϩμΫτʮLINE ΞϓϦʯͷ ◦◦ػೳՃϓϩδΣΫτʹؔΘΔ Data Science
νʔϜ
ʢνʔϜ|ϓϩδΣΫτ|ϓϩμΫτʣϚωδϝϯτ 17 νʔϜ ϓϩδΣΫτ ϓϩμΫτ ৫ͷඪΛઃఆ͠ɺͦͷ࣮ݱͷͨΊʹ ࿑ྗɾ࣌ؒɾ͓ۚͷΛௐ͠ޮԽ͢Δ ※ આ໌ͷͨΊʹ୯७Խ͍ͯ͠·͢
ʮయܕతͳ̍ͷࣄ༰ʁʯ 18 ࣌ظ ϓϩδΣΫτ A ϓϩδΣΫτ B ϓϩδΣΫτ C ͱ͋Δ
1 λεΫ͕ؒΛۭ͚ͯஅଓతʹਐߦ͢Δ e.g. ଞνʔϜͷਐߦͪɺಥൃతͳґཔ
ੳɾਪܕͷλεΫɿ՝ղܾͷαΠΫϧ ؍ଌ Ծઆͱ՝ ͷઃఆ ݕূ ղܾࡦͷཱҊ 19 ࣌ظ
εςοϓ̍. ؍ଌ ؍ଌ Ծઆͱ՝ ͷઃఆ ݕূ ղܾࡦͷཱҊ 20 ࣌ظ ࠷ۙɺΞΫςΟϒϢʔβʔ
͕ఀ͍ͯ͠Δʁ 2݄ 3݄ 4݄ 5݄ μογϡϘʔυɿ ओཁͳࣄۀࢦඪͷϞχλϦϯάද ※ ΓऔΓͱͯ͢Սۭͷͷ
εςοϓ̎. Ծઆͱ՝ͷઃఆ ؍ଌ Ծઆͱ՝ ͷઃఆ ݕূ ղܾࡦͷཱҊ 21 ࣌ظ ͜ͷΞΫςΟϒϢʔβʔͷ
ਪҠରॲ͖͢ͷ͔ʁ - ྫͷقઅతͳมಈʁ - Ϣʔβʔͷηάϝϯτ͝ͱͷมԽʁ - ৽ن / طଘ / ෮ؼ - ଞػೳͷར༻ϢʔβʔͷਪҠʁ → ʮ৽نϢʔβʔͷܧଓ͕Լ ͍ͯ͠Δɻݩͷਫ४ʹճ෮͢Δͱ ˓ສਓ૿ՃͷӨڹ͕͋Δʯ ※ ΓऔΓͱͯ͢Սۭͷͷ
εςοϓ̏. ղܾࡦͷཱҊ ؍ଌ Ծઆͱ՝ ͷઃఆ ݕূ ղܾࡦͷཱҊ 22 ࣌ظ -
৽نϢʔβʔʹϩάΠϯΛ ଅ͢௨ΛૹΖ͏ - ௨ͷසΛςετ͍ͨ͠ ςετͷઃܭΛ͠·͢ - ൱ΛධՁ͢Δࢦඪͷܾఆ - ςετʹඞཁͳαϯϓϧ αΠζͷܭࢉ - ൱ͷஅج४ͷ߹ҙ ※ ΓऔΓͱͯ͢Սۭͷͷ
εςοϓ̐. ݕূ ؍ଌ Ծઆͱ՝ ͷઃఆ ݕূ ղܾࡦͷཱҊ 23 ࣌ظ ςετͷ݁ՌΛੳ͠·͢
- σʔλͷਖ਼ৗͳऩूͷ֬ೝ - ࢦඪʹର͢ΔԾઆݕఆ - ՃͷվળҊͷࣔࠦ - ૯߹తͳϨϙʔςΟϯά Ճೖཌʹ̍ճ͚ͩ௨Λ ૹΔҊΛ࠾༻͢Δ ※ ΓऔΓͱͯ͢Սۭͷͷ
εςοϓ̍(2). ؍ଌ ؍ଌ Ծઆͱ՝ ͷઃఆ ݕূ ղܾࡦͷཱҊ 24 ࣌ظ ৽نϢʔβʔͷܧଓ
ࠓޙϞχλϦϯά͠·͢ ※ ΓऔΓͱͯ͢Սۭͷͷ 2݄ 3݄ 4݄ 5݄ 6݄ 2݄ 3݄ 4݄ 5݄ 6݄ μογϡϘʔυʹ߲ΛՃ͢Δ ΞΫςΟϒϢʔβʔ ৽نϢʔβʔܧଓ
ੳɾਪܕͷλεΫɿ՝ղܾͷαΠΫϧ ؍ଌ Ծઆͱ՝ ͷઃఆ ݕূ ղܾࡦͷཱҊ 25 ࣌ظ - ੳɾਪͷλεΫ
ؔऀͱͷίϛϡχέʔγϣϯΛ ௨ͯ͡ਐߦ͢Δ - ౷ܭͳͲઐࣝͷ׆༻ɺ શମͷαΠΫϧͷதͷҰཁૉ - ࠷ऴతͳҙࢥܾఆऀɺࣄۀɾ ϓϩμΫτɾϓϩδΣΫτͷऀ
ઐతͳֶͷࣝ͏ʁ 26 “LIFE AND MATHS”, © Pearls of Raw Nerdism
http://pearlsofrawnerdism.com/life-and-maths/
ઐతͳֶͷࣝ͏ʁ 27 “LIFE AND MATHS”, © Pearls of Raw Nerdism
http://pearlsofrawnerdism.com/life-and-maths/ ࢲͷߟ͑ɿ - ઐతͳֶͳ͠ͰࡁΉػձͷ΄͏͕ଟ͍ - ઐ͕ࣝ͋Δͱɺ՝ղܾͷ֤εςοϓͷ্࣭͕͕Δ
ઐతͳֶͳ͠ͰࡁΉػձͷ΄͏͕ଟ͍ ൃੜස ֶతͳ ෳࡶ 28 ߴ ߴ ֓೦ਤ
ੳɾਪܕͷׂ ˚ෳࡶͳ͜ͱΛߦ͢Δ͜ͱ ˚ཧతʹ৽نͳ͜ͱΛߦ͏͜ͱ ✓ ࣄۀʹཱͭݟΛదʹఏڙ͢Δ͜ͱ ʮࣄۀʹର͢Δߩݙʯ ʮ࣮ࢪʹཁ͢Δίετʯͷ͕࣠ӅΕ͍ͯΔ ֶతͳ͠͞ ≠ ࣄۀ্ͷ՝ղܾͷ͠͞
ฏқͳ࡞ۀɺઐతͳۀͷ ྫɿ୯७ͳूܭ࡞ۀ ઐࣝΛ ൃش͢Δۀ 29 ฏқͳ࡞ۀΛ௨ͨ͡σʔλɾࣄۀͷशख़ → ઐࣝΛൃش͢Δۀͷߦ ࣄۀʹର͢Δཧղͷ্ →
࣮ࢪʹίετͷ͔͔ΔੳλεΫͷड
ઐ͕ࣝ͋Δͱɺ՝ղܾͷ֤εςοϓͷ্࣭͕͕Δ 30 ؍ଌ Ծઆͱ՝ͷઃఆ ݕূ ղܾࡦͷཱҊ ΑΓϊΠζʹؤ݈Ͱղऍͷ͍͢͠ࢦඪΛ ఆٛͰ͖Δ ʮσʔλͱֶʹΑͬͯղ͚Δʯͷ ఆࣜԽͷϨύʔτϦʔ͕૿͑Δ
ద͔ͭޮతͳղܾࡦΛબͰ͖Δ ҙࢥܾఆʹඞཁͳݟΛత֬ʹநग़Ͱ͖Δ
֓೦ͷ֫ಘੈքͷݟ͑ํΛม͑Δ 31 ՝ɿ̎Λ̍ສݸͨ͑͠ΛΓ͍ͨ ࢉͷ֓೦ΛΒͳ͍ͱ 2 + 2 + 2 +
2 + …… ʮݱ࣮తͳ࣌ؒͰղܾͰ͖·ͤΜʯ ࢉΛ͍ͬͯΕ 2 × 10,000 = 20,000 ղ͚ͳ͍ ղ͚Δ
ʮ͑Λग़͢ͱࣄۀʹཱͭʯྖҬΛࢦ͢ 32 ࣄۀՁʹ ݁ͼͭ͘ ࣄۀՁʹ ݁ͼ͖ͭͮΒ͍ ͑Λग़ͤΔ ͑Λग़ͤͳ͍ ઐࣝͷशಘ ࣄۀͷཧղ
ؔऀͱͷର Cf. ҆ਓ, ʮΠγϡʔ͔Β͡ΊΑʕతੜ࢈ͷʰγϯϓϧͳຊ࣭ʱʯ, ӳ࣏ग़൛ʢ2010ʣ σʔλαΠΤϯςΟετͷۀ্ͷλεΫΛ̎࣍ݩʹϚοϓ͢Δ
ࣄۀձࣾͷσʔλαΠΤϯςΟετͷࣄ 33 ࣄۀͷͨΊͷ՝ղܾͷαΠΫϧ ੳܕ / ΞϧΰϦζϜܕ / ਪܕ νʔϜͱͯ͠ϓϩδΣΫτʹऔΓΉ ྨʢҰྫʣ
Ґஔ͚ͮ ੳɾਪͷλεΫ ઐతͳࣝ ՝ղܾͷ࣭Λ্͛Δ
ͲΜͳڥͩͱྗΛൃش͍͔͢͠ʁ 34 A. αΠΤϯε͕Ͱ͖Δ͜ͱ Պֶతํ๏ʹج͍ͮͯۀΛߦ͠ɺՌ͕ೝ͞ΕΔ͜ͱ - ٬؍తͳࠜڌʹج͍ͮͯɺཧΛల։͢Δ͜ͱ - खଓ͖͕ه͞Εɺ࠶ݱՄೳͰ͋Δ͜ͱ -
ͱ͘ʹ͕݁ޡΓͩͬͨ߹ʹɺݕূՄೳͰ͋Δ͜ͱ
ۀΛαΠΤϯεʹ͢ΔͨΊʹ 1. ܧଓ͢Δ 2. ԾఆΛڞ༗͢Δ 3. ࣈΛݟΔલʹஅج४ΛܾΊΔ 35 σʔλαΠΤϯςΟετଆʹ৺͕͚Δ͖͜ͱ͕͋Δ
1. ܧଓ͢Δ 36 Պֶతํ๏ɺ܁Γฦ͢͜ͱʹҙ͕ٛ͋Δ ԿΛ͖͔͢ʁ - ࠶ݱɾݕূՄೳͳΑ͏ʹهΛ͢ - ҡ࣋Մೳͳ؍ଌํ๏Λߏங͢Δ ʢϞχλϦϯάͷࣗಈԽʣ
- ࣍ͷ՝ઃఆΛଅࣔࠦ͢Λఏڙ͢Δ
2. ԾఆΛڞ༗͢Δ 37 ܦݧՊֶʹ͓͚ΔՊֶతࣝ ✗ ઈରෆมͷਅ࣮ͷू߹ ✓ ؍ଌͱԾఆʹج͍ͮͯਪ͞Εͨؼ݁ ԿΛ͖͔͢ʁ -
ԾఆΛ໌֬ʹ͑Δ ʮϢʔβʔͷ૿Ճઌ݄ͱಉ͡ͱԾఆ͠·͢ʯ - ݕূͷεςοϓͰɺࣄલͷԾఆͷଥੑݕূ͢Δ ʮϢʔβʔͷ૿Ճɺ݁Ռతʹઌ݄ͱൺͯʙͰͨ͠ʯ
3. ࣈΛݟΔલʹஅج४ΛܾΊΔ 38 ྔ → ৗݴޠͷมʹᐆດੑ͕͋Δ ͜ͷࢦඪ͕ “ेʹ” ্ঢͨ͠Β ςετޭͱஅ͠·͠ΐ͏
ʢ+3% ”े” ͩΖ͏͔…ʣ ԿΛ͖͔͢ʁ - ࣄલʹஅج४ΛྔతʹܾΊΔ - ج४ͷࠜڌ٬؍తʹ͢Δ (ྫ) ࣄۀඪʹର͢Δظد༩ ɹɹ͡ΒΕͨίετͷճऩ ɹɹաڈͷྨࣅࣄྫͷ݁Ռ ※ ͯ͢Սۭͷͷ ࢦඪͷ্ঢ +3% Ͱͨ͠
ٳܜ 39
શମͷߏ 1. ରΛΔɿ ࣄۀձࣾͷσʔλαΠΤϯςΟετͬͯͲΜͳࣄʁ ʢٳܜʣ 2. தΛΔɿ ࣮ͰΑ͘༻͍Δ౷ܭͷҰ෦ͱ۩ମతͳࣄྫ 40
2. தΛΔɿ ࣮ͰΑ͘༻͍Δ౷ܭͷҰ෦ͱ۩ମతͳࣄྫ 41
ੳɾਪͷ۩ମతͳࣄʢ࠶ܝʣ 42 ਐߦத ࣄલ ࣄޙ ࣌ظ Ωϟϯϖʔϯ / ৽ػೳͷՃ /
طଘػೳͷมߋͳͲ ΦϯϥΠϯ A/B ςετ 1. αϯϓϧαΠζͷܭࢉ 2. ଟॏൺֱ 3. ׳ΕޮՌͷਪఆ
1. αϯϓϧαΠζͷܭࢉ 43 ςετରͷࠩΛݕఆ͢ΔͨΊʹඞཁͳαϯϓϧαΠζΛࢉग़͢Δ͜ͱ Q. αϯϓϧαΠζΛܭࢉܾͯ͠ΊΔཧ༝ʁ A. ౷తͳԠ༻Ͱɺαϯϓϧऩूͷίετ͕ߴ͔ͬͨ ɹe.g. ྟচݚڀɺۀ
Q. ΣϒαʔϏεͳΒαϯϓϧऩूͷίετߴ͘ͳ͍ͷͰʁ
ΣϒαʔϏεͰαϯϓϧαΠζΛܭࢉ͢Δཧ༝ 44 1. աʹେ͖ͳαϯϓϧαΠζ → খ͞ͳมԽͰ༗ҙʹͳΓ͕ͪ ʮ౷ܭతʹ༗ҙʯڧ͍ҹΛ༩͑Δදݱ 2. ಛʹςετҊ͕ྑ͘ͳ͍࣌ɺϢʔβʔʹແ༻ͳӨڹΛ༩͑ͯ͠·͏ 4.
P-Hacking ͷ༨͕Δ ʮ༗ҙ͕ࠩग़ͳ͔͔ͬͨΒαϯϓϧαΠζΛେ͖ͯ͘͠࠶ςετ͠Α͏ʯ 3. SUTVA (Stable Unit Treatment Value Assumption) ͕ഁΕ͘͢ͳΔ ʮ͋ΔϢʔβʔͷߦಈଞͷϢʔβʔͷׂΓͯʹӨڹ͞Εͳ͍ʯ ʢྫʣςετը໘͕ڞ༗͞ΕΔɺϝσΟΞʹऔΓ্͛ΒΕΔ
αϯϓϧαΠζܭࢉͷجຊܗ 45 ઃఆ - ಠཱͳ̎܈αϯϓϧͷฏۉͷݕఆ - ࢄ̎܈Ͱಉ͡ & ط -
αϯϓϧαΠζઍ ~ ສ݅ఔऔΕΔ ݕఆ͞ΕΔԾઆ - ؼແԾઆ - ରཱԾઆ H0 H1 μ1 − μ2 = 0 μ1 − μ2 ≠ 0 αϯϓϧαΠζɹͷܾఆʹඞཁͳύϥϝʔλ - ༗ҙਫ४ - ݕग़ྗ - ޮՌྔ - ࢄ α 1 − β δ = μ1 − μ2 σ2 < + ∞ n ʢɹ ͕ਅͷ߹ʣ H1
αϯϓϧαΠζܭࢉͷ෮शʢ̍ʣ 46 ਤԼهจݙΑΓ࠶ߏͨ͠ Gerald van Belle, “Statistical Rules of Thumb”
(2nd edition), Wiley, 2008 ඪຊฏۉͷࠩ x1 − x2 H0 : μ1 − μ2 = 0 0 S . E . = σ 2 n ਖ਼نͷ࠶ੜੑΑΓ α 2 α 2 ༗ҙਫ४ɹɿ α ɹ͕ਅͷͱ͖ɹ Λ࠾ͯ͠͠·͏֬ ʢِཅੑʣ H0 H1
αϯϓϧαΠζܭࢉͷ෮शʢ̎ʣ 47 ਤԼهจݙΑΓ࠶ߏͨ͠ Gerald van Belle, “Statistical Rules of Thumb”
(2nd edition), Wiley, 2008 ඪຊฏۉͷࠩ x1 − x2 H1 : μ1 − μ2 = δ δ S . E . = σ 2 n H0 : μ1 − μ2 = 0 0 β = 1− ݕग़ྗ (1 − β) ɹ͕ਅͷͱ͖ɹ Λ࠾ͯ͠ ͠·͏֬ʢِӄੑʣ H0 H1 β
αϯϓϧαΠζܭࢉͷ෮शʢ̏ʣ 48 ਤԼهจݙΑΓ࠶ߏͨ͠ Gerald van Belle, “Statistical Rules of Thumb”
(2nd edition), Wiley, 2008 ඪຊฏۉͷࠩ x1 − x2 δ S . E . = σ 2 n 0 β n* = 2σ2 (z1−α/2 + z1−β) 2 δ2 㱺 ཁ݅Λຬͨͨ͢Ίʹ ࠷ݶඞཁͳαϯϓϧαΠζ z1−α/2 σ 2 n* = δ − z1−β σ 2 n* ඪ४ਖ਼نͷ Ґؔ α 2
1. ਅͷͷࢄɹ ͕େ 2. ِཅੑɺِӄੑΛ͑Δ ɹɹ͕খ 3. ݕग़͍ͨ͠ޮՌྔɹ͕খ ͕େ͖͘ͳΔཁҼ n*
σ2 α, β δ αϯϓϧαΠζʹ͍ͭͯͷิ 49 n* = 2σ2 (z1−α/2 + z1−β) 2 δ2 ཁ݅Λຬͨͨ͢Ίʹ ࠷ݶඞཁͳαϯϓϧαΠζ ύϥϝʔλͷܾΊํʢҰྫʣ ɹɹɿ׳शతͳ͔ɺݫ͠ʹ ɹɹɿۙͷ࣮ଌ ɹɹʢςετޙʹଥੑΛ֬ೝʣ ɹɹɿ׳शతͳ ɹɹɹor ίετΛ্ճΔޮՌ ɹɹɹor աڈͷྨࣅ͢Δςετ݁Ռ α, β σ2 δ
2. ଟॏൺֱ 50 ࣮Ͱɺ̏܈Ҏ্ͷൺֱΛٻΊΒΕΔ͜ͱ͕Α͋͘Δ 㲗 લઅͰ෮शͨ̎͠܈ؒͷݕఆ എܠ ظؒͰͳΔ͘ଟ͘ͷՄೳੑΛςετ͍ͨ͠ ܈ؒϖΞͷճ͚ͩ୯७ʹݕఆΛ܁Γฦ͍͚ͯ͠ͳ͍ ʂʂ
ଟॏൺֱ ͷ →
࣮ྫɿάϧʔϓ࡞ը໘ͷมߋςετ 51 “ίϛϡχέʔγϣϯΞϓϦʮLINEʯͷػೳվળΛࢧ͑ΔσʔλαΠΤϯε” LINE DEVELOPER DAY 2019 https://linedevday.linecorp.com/jp/2019/sessions/B1-3 άϧʔϓ࡞ͷखॱΛɺΑΓ͍͘͢վྑ͍ͨ͠
̎ͭͷมߋΛΈ߹Θͤͯࢼ͍ͨ͠ 52 “ίϛϡχέʔγϣϯΞϓϦʮLINEʯͷػೳվળΛࢧ͑ΔσʔλαΠΤϯε” LINE DEVELOPER DAY 2019 https://linedevday.linecorp.com/jp/2019/sessions/B1-3 1. ࠷ۙτʔΫͨ͠༑ͩͪΛ༏ઌදࣔ͢Δ
2. खॱΛ̍ը໘ʹ·ͱΊΔ
̎×̎=̐௨Γ̒ϖΞͷݕఆʁ 53 “ίϛϡχέʔγϣϯΞϓϦʮLINEʯͷػೳվળΛࢧ͑ΔσʔλαΠΤϯε” LINE DEVELOPER DAY 2019 https://linedevday.linecorp.com/jp/2019/sessions/B1-3 গͷީิͰɺৄࡉͳݕূҙ֎ͳ΄ͲෳࡶʹͳΔ
ݕఆͷ܁Γฦ͠Կ͕͔ʁ 54 ݕఆ͞ΕΔԾઆ - ؼແԾઆ - ରཱԾઆ H0 H1 θ1
= θ2 = θ3 = θ4 ʢ̐܈ͷ߹ʣ {θi} i=1,2,3,4 ͷ͏ͪগͳ͘ͱ̍ͭͷϖΞͰ θi ≠ θj (i ≠ j) ࣮ߦతͳ༗ҙਫ४ Family-Wise Error Rate α = 1 − (1 − α)6 ≥ α α α 1 − (1 − α)6 શମͱͯ͠ݟͨ࣌ʹɺِཅੑ্͕͕ͬͯ͠·͏
Bonferroni ิਖ਼ 55 ֤ϖΞͷݕఆͷ༗ҙਫ४ΛɺݕఆͷճɹͰׂͬͨʹௐ͢Δ α → α m m Family-Wise
Error Rate α ≤ α ͱͳΓɺશମͱͯ͠ͷ༗ҙਫ४͕อͨΕΔ σϝϦοτ ͕େ͖͍ͱอकతʹͳΓ͕ͪʢِӄੑͷ্ঢʣ m
άϧʔϓ࡞ը໘ςετͰͷରॲ 56 “ίϛϡχέʔγϣϯΞϓϦʮLINEʯͷػೳվળΛࢧ͑ΔσʔλαΠΤϯε” LINE DEVELOPER DAY 2019 https://linedevday.linecorp.com/jp/2019/sessions/B1-3 - ࣄલݕূʹج͍ͮͯɺରΛ̏܈̎ϖΞʹߜΔ
- ̎ϖΞʹରͯ͠ Bonferroni ิਖ਼ͯ͠ݕఆ͢Δ α → α/2
άϧʔϓ࡞ը໘ςετͷ݁Ռ 57 “ίϛϡχέʔγϣϯΞϓϦʮLINEʯͷػೳվળΛࢧ͑ΔσʔλαΠΤϯε” LINE DEVELOPER DAY 2019 https://linedevday.linecorp.com/jp/2019/sessions/B1-3 ʮ̎ը໘ +
࠷ۙτʔΫͨ͠༑ͩͪϦετʯ → ࡞ྃΛҡ࣋ͭͭ͠ɺ࡞ͷॴཁ࣌ؒΛॖͨ͠
ଟॏൺֱͷରॲʹऔΓೖΕ͍ͯΔ͜ͱ 58 - جຊతʹൺֱରΛߴʑ̐ύλʔϯʹݶఆ͢Δ ̑ύλʔϯҎ্ੳղऍ͘͠ͳΔ - Bonferroni ิਖ਼ͰِཅੑΛ੍͢Δ ϦεΫͷ͋ΔςετͰِཅੑΛආ͚͍ͨ -
σϝϦοτΛิ͏ͨΊɺݕग़ྗɹΛߴΊʹઃఆ͢Δ β
3. ׳ΕޮՌͷਪఆ 59 ΦϯϥΠϯςετͷظؒ௨ৗ̎ʙ̏िؒ ظؒͷԠΛͦͷ··ड͚औͬͯΑ͍ͷͩΖ͏͔ʁ Ծઆ ಛʹྺ࢙͕͘श׳Խ͍ͯ͠Δػೳ΄Ͳɺ ը໘ͷมߋʹର͢ΔҰ࣌తͳԠ͕ݱΕΔ ՝ Ұ࣌తͳԠ͕ఆৗతͳར༻ʹམͪண͘
ʮ׳ΕޮՌʯΛݕग़͍ͨ͠
࣮ྫɿ༑ͩͪՃը໘ͷγϯϓϧԽςετ 60 “ίϛϡχέʔγϣϯΞϓϦʮLINEʯʹ͓͚Δ࣮ફతσʔλαΠΤϯε” DEIM 2020 https://engineering.linecorp.com/ja/blog/deim2020-report/ - ༑ͩͪՃը໘͔Βɺ༑ͩͪՃҎ֎ͷΞΠςϜΛআ͢Δ - ༑ͩͪՃ
& LINEެࣜΞΧϯτՃ͕ݮগͯ͠͠·ͬͨ
LINE ͷ৽نϢʔβʔͷΈʹݶఆͯ͠ܭଌͯ͠ΈΔ 61 - ༑ͩͪՃɾLINEެࣜΞΧϯτՃͱʹ༗ҙͳมԽͳ͠ - ԾઆɿશମʹطଘϢʔβʔ͕׳ΕΔ·ͰͷԠ͕ݱΕ͍ͯΔʁ “ίϛϡχέʔγϣϯΞϓϦʮLINEʯʹ͓͚Δ࣮ફతσʔλαΠΤϯε” DEIM 2020
https://engineering.linecorp.com/ja/blog/deim2020-report/
׳ΕޮՌΛࠩͷࠩͰϞσϧԽ͢Δ 62 1st half 2nd half Control Treatment yT,1 yC,1
yC,2 yT,2 ׳ΕޮՌҎ֎ͷӨڹ̎܈ͰಉҰ ʢฒߦτϨϯυ & ڞ௨γϣοΫͷԾఆʣ Ծఆ ςετظؒΛલɾޙʹ̎͢Δ ࠩͷࠩ౷ܭྔ δ = (yT,2 − yC,2) − (yT,1 − yC,1) ճؼϞσϧԽ ̂ β3 = ̂ δ y = β0 + β1 T + β2 S + β3 TS + ε T / C ͷμϛʔ 1st / 2nd ͷμϛʔ Ͱ͋Γɺ ճؼϞσϧͷͯ·Γ & ͷ༗ҙੑΛ֬ೝ͢Δ
༑ͩͪՃը໘ͷγϯϓϧԽςετͷ݁Ռ 63 - LINEެࣜΞΧϯτՃͷมԽʹɺ׳ΕޮՌ͕ݱΕ͍ͯͨ - LINEެࣜΞΧϯτՃͷͷΈআͯ͠ɺϦϦʔε͞Εͨ “ίϛϡχέʔγϣϯΞϓϦʮLINEʯʹ͓͚Δ࣮ફతσʔλαΠΤϯε” DEIM 2020 https://engineering.linecorp.com/ja/blog/deim2020-report/
ख๏Λඪ४Խͯ͠ਫฏల։͢Δ 64 “σʔλαΠΤϯε͕ಋ͘τʔΫϝχϡʔUIͷϦχϡʔΞϧϓϩδΣΫτ” LINE DEVELOPER DAY 2020 https://linedevday.linecorp.com/2020/ja/sessions/3932 - ׳ΕޮՌݕग़๏ɺτʔΫϝχϡʔͷϦχϡʔΞϧͰ׆༻ͨ͠
- ۀΛαΠΤϯεʹ͢ΔͨΊʹ - ܧଓ͢Δ
࣮ͰΑ͘༻͍Δ౷ܭͷҰ෦ͱ۩ମతͳࣄྫ 65 ΦϯϥΠϯ A/B ςετ 1. αϯϓϧαΠζͷܭࢉ 2. ଟॏൺֱ 3.
׳ΕޮՌͷਪఆ
͓ΘΓʹ 66
ʮσʔλαΠΤϯςΟετʯͷকདྷʁ 67 ࢲͷߟ͑ɿ - ʮσʔλαΠΤϯςΟετʯͱ͍͏ݺশظมΘ͍͔ͬͯ͘ - σʔλͱֶΛͬͯ՝Λղܾ͢Δͱ͍͏ཁ໘ന͞ ˠ ݺশͷਰΑΓͣͬͱ͘ଓͩ͘Ζ͏
ੈք͔ΒࣝΛநग़͢ΔαΠΫϧ 68 ੈք σʔλ ࣝ ॲཧ݁Ռ ࣝΛͲ͏ੈքʹϑΟʔυόοΫ͢Δ͔ʁ ੈքʹͲ͏͋ͬͯ΄͍͔͠ͱ͍͏Ձஅ ԿΛɺԿͷͨΊʹͲ͏؍ଌ͢Δ͔ʁ ܭଌʹ͢Δ͔൱͔ͱ͍͏Ձஅ
ରͷੈքΛݶఆ͢ΕࣗಈԽͷՄೳੑ͕͋Δ 69 ੈք σʔλ ࣝ ॲཧ݁Ռ ͷΓग़͠ͱγεςϜԽɺιϑτΣΞΤϯδχΞϦϯάͷྖҬ ʢྫʣهࣄͷਪનɺϚʔέςΟϯά ՁஅͷॏཁੑΓଓ͚Δ
ֶੜͷօ͞Μͷϝοηʔδ 70 ֶͼଓ͚·͠ΐ͏ औΓΈ·͠ΐ͏ - ֶ෦ɾେֶӃͰͷݚڀʢ՝ղܾͷαΠΫϧʣ ɹେֶੈքϨϕϧͷઐՈ͔ΒֶΔوॏͳॴ - ֶɾֶͷઐࣝ -
ϓϩάϥϛϯά - ޠֶ - ٕज़ྙཧɺ๏੍ɺྺ࢙ɺ… ৬໊τϐοΫͷྲྀߦʹͱΒΘΕ͗ͣ͢ɺ ઐࣝͰ՝ղܾͰ͖ΔਓΛͥͻࢦ͍ͯͩ͘͠͞