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Kaggle M5-Forecasting (Walmart)

Kaggle M5-Forecasting (Walmart)

先日開催された、Kaggle(M5-Forecasting)の当方のSolution資料です。

IHiroaki

July 19, 2020
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  1. LBHHMFOBNF*)JSPBLJ
    .'PSFDBTUJOH
    "DDVSBDZ6ODFSUBJOUZ

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  2. ໨࣍ɿ
    1. ࣗݾ঺հ
    2. ݁Ռ
    3. ࠓճͷऔΓ૊Έͱߟ͑
    4. Ϟσϧ֓ཁ
    5. σʔλ୳ࡧ
    6. ಛ௃ྔબ୒
    7. Ϟσϧৄࡉ
    8. ൓লͱ՝୊

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  3. ̍ɽࣗݾ঺հ

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  4. ̎ɽ݁Ռ
    ίϯϕͷ֓ཁ͸ͪ͜ΒΛࢀরɿhttps://www.kaggle.com/c/m5-forecasting-accuracy/overview
    ίϯϕͷ֓ཁ͸ͪ͜ΒΛࢀরɿhttps://www.kaggle.com/c/m5-forecasting-uncertainty/overview

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  5. ̏ɽࠓճͷऔΓ૊Έ
    ͱߟ͑
    ʻऔΓ૊Έʼ
    ɾॳίϯϖɻ
    ɾ3݄த०ʙ6݄຤ͷίϯϖऴྃ·Ͱͷ̏ϲ݄൒΄΅ٳΈͳ͠ͰରԠɻ
    ɾҰ೔ฏۉ̍̎ʙ̍̒࣌ؒΛίϯϖʹ๋͛Δɻ
    ʻߟ͑ʼ
    Accuracyɿ
    ɾ༧ଌ஋Λ࢖ͬͨಛ௃ྔٴͼલ೔ͷ༧ଌ஋Λ༻͍ͨཌ೔ͷ༧ଌʢ࠶ؼతΞϓϩʔνʣ౳͸ߦΘͳ͍ɻʢಛʹ࠶ؼత
    Ξϓϩʔν͸̎ɺ̏೔ͷ༧ଌͳΒ༗ޮ͔΋͠Εͳ͍͕̎̔೔ͷ༧ଌͩͱޡࠩͷ஝ੵ͕େ͖͘ͳΓ͗͢ΔՄೳੑ͕͋
    Δɻʣ
    ɾ௚લͷ28೔ؒ΋TrainDataͱͯ͠࢖༻͢Δɻʢաֶशɺֶशෆ଍ͷڪΕ͕͋Δ͜ͱ͔Β஫ҙΛ෷͍ϞσϧΛ࡞੒͢
    Δඞཁ͕͋Δɻʣ
    Uncertaintyɿ
    ɾAccuracyͰͷ࠷ऴఏग़஋Λ۝෼Ґ఺ͷ̑̌ˋ఺ͱ͢Δɻ
    ɾAccuracyϞσϧʹ͓͚ΔValidationظؒͷ࣮੷ͱ༧ଌ஋ͱͷֹࠩΛෆ࣮֬ੑͱͯ͠࢖༻͢Δɻ
    ɾΑͬͯAccuracyʹ͓͍ͯ൚Խੑೳͷߴ͍Ϟσϧͷ࡞੒͕ॏཁͱͳΔɻ

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  6. ̐ɽϞσϧ֓ཁ "DDVSBDZ 6ODFSUBJOUZ
    Ϟσϧɿ LightGBMͷΈΛ࢖༻
    Ϟσϧߏ଄ : 

    28೔Λਖ਼֬ʹ༧ଌ͢ΔͨΊʹ1೔ຖʹݸผͷϞσϧ
    Λ࡞੒ɻ·ͨϝϞϦͷ໰୊΋͋Γɺstore_idຖʹϞ
    σϧΛ෼ׂɻ߹ܭ 28 day × 10 id = 280 models
    ॏཁͳಛ௃ྔ:
    ಛ௃ྔʹؔͯ͠͸͋·Γಛผͳ΋ͷ͸ͳ͘ඪ४త
    ͳ΋ͷͷΈͱͳͬͨɻ
    ex) Basic Lagʢmean, max, ,min, std, medianʣ
    Average Encoding ʢ֤Ϩϕϧຖʣ
    IDʢTrainDataʹͯ༩͑ΒΕͨIDʣ
    ֶश࣌ؒɿ
    8೔ʙ9೔ʢՄೳͳݶΓϦεΫΛഉআ্ͨ͠Ͱͷ࣌
    ؒʣ
    ※ֶश࣌ؒΛ୹ॖ͢ΔͨΊͷํ๏ɻʢ༧ଌ͕গ͠ߥ͘ͳΔ͕ͦ͜·Ͱ
    μϝʔδ͕ͳ͍΋ͷʣ
    ɾLearningRateΛେ͖͘͠ɺnum_iterΛݮΒ͢ɻʢlr0.03ͳΒ
    iter500~700ఔ౓ʣ
    ɾBasicLagಛ௃ྔΛ࡟আ͢Δɻʢಛʹmulti_2, 3, 5, ʣ
    ɾstore_id୯ҐϞσϧΛͳ͘͢ɻʢͨͩ͠ಛ௃ྔΛे෼ݮΒ͞ͳ͍ͱϝ
    ϞϦͷ໰୊ൃੜʣ
    Ϟσϧ : AccuracyΛ࡞੒͢Δࡍʹ࢖༻ͨ͠Model
    Λ࢖༻ɻ
    ࢉग़ํ๏ :
    ۝෼Ґ఺ͷ͏ͪ̑̌ˋ఺ʹؔͯ͠͸Accuracyͷ
    Final SubmissionΛ࢖͏ɻ
    ͦͷଞ̔఺ʹؔͯ͠͸Accuracyʹͯࢉग़ͨ͠
    Validationظؒʹ͓͚Δ࣮੷ͱ༧ଌ஋ͷࠩΛෆ֬
    ࣮ੑͱ͠ɺల։͢Δɻ

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  7. ̑. σʔλ୳ࡧ
    ച্ݸ਺ͷϓϩοτʢ߹ܭʣ
    Ұݟ͢Δͱશମʹ౉ͬͯ
    ্ঢ܏޲Ͱ͋ΔΑ͏ʹݟ
    ͑Δɻ
    ೔ຖͷొ࿥ΞΠςϜ਺
    ೔ຖʹΞΠςϜ͕௥Ճ͞Ε͓ͯΓ
    Totalͷ্ঢ܏޲ͷཁҼͱͳ͍ͬͯΔ͜ͱ
    ͕૝ఆ͞ΕΔɻ
    30490
    ʢ̍ʣτϨϯυ
    ্ਤɿ೔ຖͷച্ݸ਺ͷ߹ܭਪҠ
    Լਤɿ೔ຖͷΞΠςϜొ࿥਺ਪҠ
    ্ਤΛݟΔͱҰݟ௨ظʹΘͨͬͯ૿Ճ͠
    ͍ͯΔΑ͏ʹݟ͑Δ͕ԼਤͰΞΠςϜ͕
    ೔ʑొ࿥͞Ε͍ͯΔ͜ͱ͕Θ͔Δɻ
    Αͬͯ͜ΕΒͷ৽͘͠ೖͬͨΞΠςϜʹ
    ΑΓ্ঢ܏޲͕ݟΒΕΔ͜ͱ͕ߟ͑Β
    Εɺ͜ͷ৔߹্ਤͰ͸܏޲Λଊ͑Δ͜ͱ
    ͕Ͱ͖ͳ͍ɻ
    Αͬͯ࣍ʹΞΠςϜొ࿥೥ผʢച্։࢝
    ೥ʣͷ೔ຖͷച্ݸ਺ͷ߹ܭਪҠΛݟͯ
    ΈΔɻ

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  8. ̑. σʔλ୳ࡧ
    ച্։࢝೥ผͷച্ݸ਺ͷϓϩοτ
    ਤɿച্։࢝೥ผͷചΓ্͛ݸ਺ͷ߹ܭਪ
    Ҡ
    Ͳͷਤʹ͓͍ͯ΋2015೥લ൒·Ͱ͸ݮগ܏
    ޲ʹ͋Δͷʹ΋͔͔ΘΒͣɺ2015೥ޙ൒͔
    Β2016೥ʹ͔͚ͯ૿Ճ͍ͯ͠Δ͜ͱ͕Θ͔
    Δɻ
    ͜Ε͸Կ͔͠ΒτϨϯυ͕มΘͬͨ͜ͱΛ
    ද͍ͯ͠ΔՄೳੑ͕͋ΓValidationͷऔΔظ
    ؒ΍Ϟσϧͷߏஙํ๏ʹؾΛ͚ͭΔඞཁ͕
    ͋Δɻ
    ͔͠͠ɺاۀଆͷԿ͔ࢼ࡞ʹΑΔ΋ͷͳͷ
    ͔ɺফඅτϨϯυʹΑΔ΋ͷͳͷ͔͕ෆ໌
    Ͱ͋ΓɺࠓճͷίϯϖΛߟ͑Δ্Ͱ೉͍͠
    ͱ͜Ζͱͳͬͨɻ
    ʢ̍ʣτϨϯυ
    2011 2012 2013 2014 2015 2016

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  9. ̑. σʔλ୳ࡧ
    ਤɿ28೔ຖͷച্ݸ਺ͷ߹ܭਪҠʢάϥϑ
    ͸store_idຖ͓Αͼച্։࢝೥ຖͰ͋Δʣ
    28೔ؒʹ͓͚Δ߹ܭച্ݸ਺ͷਪҠ͸Ͳ͏
    มಈ͍ͯ͠Δͷ͔ΛݟͨάϥϑͰ͋Δ͕ɺ
    ΍͸Γ঎඼धཁ͸͋Δఔ౓ҰఆͰ͋Δ͜ͱ
    ͔Β͔ɺٸܹͳ্ঢͷ͋ͱͷ28೔͸͋Δఔ
    ౓཈͑ΒΕௐ੔͞Ε͍ͯΔΑ͏ʹݟ͑Δɻ
    xʹ̓̌͸PublicLBظؒͰ͋Δ͕ଟ͘ͷάϥ
    ϑͰٸܹͳ্ঢΛԋ͍ͯ͡Δɻ
    Αͬͯݟͨ໨Ͱ༧૝͢ΔʹɺPrivateظؒͷ
    28೔ؒͷ߹ܭച্ݸ਺͸PublicLBظؒʹൺ
    ΂ͯݮগ͢ΔՄೳੑ͕͋Δఔ౓͋Δ͜ͱ͕
    ૝૾Ͱ͖Δɻ
    ʢ͜Εʹؔͯ͠͸Lagಛ௃ྔͷRollingʹͯ
    Ϟσϧʹ৫ΓࠐΊΔ͔ʁʣ
    ̎̔೔ຖͷച্ݸ਺ͷϓϩοτʢstore_idຖʣ
    ʢ̍ʣτϨϯυ

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  10. ̑. σʔλ୳ࡧ
    ̎̔೔ຖͷച্ݸ਺ͷϓϩοτʢstore_idຖʣ
    ʢ̍ʣτϨϯυ

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  11. ̑. σʔλ୳ࡧ
    ਤɿ֤ΞΠςϜʹ͓͚Δ͍Ζ͍Ζͳθϩ
    ͷύλʔϯΛάϥϑԽͨ͠΋ͷɻ
    DiscussionͰ͸θϩύλʔϯʹର͢Δҙ
    ݟ͕ඇৗʹଟ͔ͬͨͱࢥ͏ɻ
    ࠓճͷ࣌ܥྻʹ͸ଟ͘ͷθϩ͕͋Δ͕ઓ
    ུతɺඞવతͳθϩ͕ଟؚ͘·Ε͍ͯ
    ͨɻ
    اۀʹ͸ࡏݿઓུɺ঎඼ઓུ͕͋ΓͦΕ
    Β͸ຖ೥มΘΓ͏ΔɻͦͷͨΊࡏݿઓ
    ུɺ঎඼ઓུ͕Θ͔Βͳ͍ঢ়ଶͰθϩύ
    λʔϯΛ༧ଌ͢Δ͜ͱ͸ͦΕͳΓʹϦε
    Ϋ͕͋Δͱײ͡Δɻ
    ·ͨࡏݿ੾Ε΍ͨ·ͨ·ച্͕ͳ͔ͬͨ
    ͳͲͷθϩΛ༧૝͢Δʹͯ͠΋ධՁࢦඪ
    ্1೔ͷζϨ΋ڐ͞Εͳ͍͜ͱ͔Βɺ΍
    ͸ΓθϩύλʔϯΛ༧૝͢ΔϦεΫ͸େ
    ͖͍ɻ
    ࡏݿઓུɺ঎඼ઓུΛ஌Βͳ͍ঢ়ଶͰθϩύλʔϯΛ༧૝
    ͢΂͖Ͱͳ͍ʁ
    ?
    Change
    strategy? Irregular
    Long term
    ʢ̎ʣ͍Ζ͍Ζͳθϩύλʔϯ

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  12. ̒. ಛ௃ྔબ୒
    ॏཁͳಛ௃ྔ
    ɾجຊతͳLagಛ௃ྔ
    ɹˠstore_id × item_idʹ͓͚ΔLagಛ௃ྔ
    ɹˠstore_id × item_id͔༵ͭ೔୯Ґʹ͓͚ΔLagಛ௃ྔ
    ɾฏۉ஋
    ɹˠstore_id × item_id, state_id × item_id, item_idʹ͓͚Δ༵೔୯Ґͷฏۉ஋ʢ݄ʙ೔ʣ
    ɹˠstore_id × item_id, state_id × item_id, item_idʹ͓͚Δ೔෇୯Ґͷฏۉ஋ʢ̍ʙ̏̍ʣ
    ɾՁ֨มಈ
    ɾTrainDataʹͯ༩͑ΒΕͨID
    ࢼ͕ͨ͠͏·͍͔͘ͳ͔ͬͨಛ௃ྔ
    ɾ༧ଌ஋Λ࢖༻ͨ͠ಛ௃ྔ(ച্θϩύλʔϯΛ਺஋Խͨ͠ಛ௃etc…)
    ɾΫϥελϦϯάʹΑΔ৽ͨͳΧςΰϦ෼͚ʢྨࣅ঎඼ɺิ׬঎඼ʣ
    ɾ֎෦σʔλ
    etc…..

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  13. ̒. ಛ௃ྔબ୒
    pred_day1
    1೔໨ͷϞσϧͱ28೔໨ͷϞσϧ͸ॏཁ౓
    ͕ߴ͍ಛ௃ྔ͕͔ͳΓҟͳΔɻ
    1೔໨ʹ͍ۙ΄ͲLagܥ͕ߴ͘ɺ28೔໨ʹ
    ۙͮ͘΄Ͳฏۉ஋΍IDͳͲͷΑΓҰൠԽ
    ͞Εͨಛ௃ྔͷॏཁ౓্͕͕Δɻ
    ϞσϧΛ28ݸʹ෼͚Δ΂͖ࠜڌʹ΋ͳ
    Δɻ
    ※ಛ௃ྔ໊ͷઆ໌͸࣍ͷεϥΠυ
    Feature Importance Plot - Top 20
    pred_day28

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  14. ̒. ಛ௃ྔબ୒
    ಛ௃ྔ໊ͷઆ໌
    • sales_residual_diff_28_roll_365 : Targetʢৄࡉ͸࣍ͷεϥΠυʣ
    • multi_5_sales_residual_diff_28_roll_365_shift_1_roll_4_mean :
    Code: df[“Target_shift_1”] = df.groupby([“id”])[“Target”].transform(lambda x : x.shift(1))
    df.groupby([“id”, “multi_5”])[“Target_shift_1”].transform(lambda x: x.rolling(4).mean())
    • private_sales_residual_diff_28_roll_365_enc_week(day)_LEVEL12_mean:
    privateɿϓϥΠϕʔτظؒͷ௚લ·ͰͷσʔλΛ࢖༻͢Δɻ
    enc_week(day)_LEVEL12_meanɿLEVEL12ͷ༵೔(೔)ͷฏۉചΓ্͛
    • sell_price_minority12 : sell_priceͷগ਺ୈҰҐͱೋҐ
    ex) 10.58345 => 58
    • id_serial : ֤ID୯Ґʹઃఆͨ͠0 ~ 30489ͷ࿈൪

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  15. ̓. Ϟσϧৄࡉ
    TARGET = TARGET - TARGET.shift(28).rolling(365)
    ʢ̍ʣτϨϯυআڈ
    ܾఆ໦ܥͷϞσϧΛ࢖͏৔߹ɺকདྷ༧ଌ
    Λ͢Δʹ͸τϨϯυΛ೷͘ඞཁ͕͋Δͱ
    ͍ͬͨ಺༰͕Discussionʹ΋͋ͬͨΑ͏
    ʹࠓճ༩͑ΒΕͨσʔλͷτϨϯυΛऔ
    Γআ͘͜ͱʹͨ͠ɻ
    ͔͠͠ɺػցֶशͳͲͰ༧૝ͨ͠༧ଌ஋
    ΛτϨϯυআڈͷࡐྉͱͯ͠࢖͏͜ͱ͸
    ϦεΫ͕͋ΔͨΊ࢖༻ͨ͘͠ͳ͔ͬͨɻ
    ࣮ࡍ༧ଌ஋ʹΑΔτϨϯυͷআڈ΋ࢼ͠
    ͕ͨτϨϯυʹ౰ͯ͸ΊΔ਺ࣜʹΑΓɺ
    কདྷͷ༧ଌ஋ʹେ͖ͳ͕ࠩ͋ͬͨɻ
    ͦͷͨΊ࣮੷Λ࢖ͬͨআڈΛߟ͑ΔதͰ
    Ұ൪҆ఆ͍ͯͨ͠TARGET͔Β
    TARGET.shift(28)rolling(365)Λݮͨ͡΋
    ͷΛTARGETͱ͢Δ͜ͱͱͨ͠ɻ
    ͔͠͠ɺ࣮੷Λ࢖ͬͨͨΊ׬શʹτϨϯ
    υΛऔΓআ͚ͯ͸͓ΒͣޮՌ͸ݶఆతͰ
    ͋ͬͨͱײ͍ͯ͡Δɻ
    ͨͩखݩͰݕূ͢ΔݶΓ̎̔೔ؒͷ༧ଌ
    ͷ͏ͪޙ൒ʢ28೔͍ۙ೔ͷ༧ଌʣʹͳΔ
    ʹͭΕτϨϯυআڈޙͷํ͕҆ఆੑ͕ߴ
    ͔ͬͨɻ
    TARGET
    TARGET.shift(28).rolling(365)
    TARGET - TARGET.shift(28).rolling(365)

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  16. ̓. Ϟσϧৄࡉ

    lightgbm.Datasets( x_train, y_train, weight = myweight )
    ʢ̎ʣweight
    objective : regression
    ධՁࢦඪͰ͋ΔWRMSSEΛೋ৐ͨ͠΋ͷ
    ͷޯ഑Λܭࢉ͠܎਺Λlightgbm.Datasets
    ͷWEIGHTͱͯ͠౉ͨ͠ɻ
    WEIGHT^2÷SCALED͸͋Β͔͡Ί42840
    ݸ෼Λܭࢉ͓͖ͯ͠30490ΞΠςϜʹల։
    ͦ͠ͷ߹ܭ஋ͱͨ͠ɻ
    42840
    1
    30490
    12Ϩϕϧ
    30490
    1
    શϨϕϧʢ42840ݸʣͷʢWeight^2 ÷ ScaledʣΛ
    ܭࢉ͢Δɻ
    30490ΞΠςϜ×12Ϩϕϧʹม׵
    ὎30490Ҏ֎ͷΞΠςϜΛ֤IDΧςΰϦຖʹׂΓ
    ৼΔɻ
    Ϩϕϧํ޲ʹ߹ܭΛࢉग़͢Δɻ

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  17. ̓. Ϟσϧৄࡉ

    ʢ̏ʣΠςϨʔγϣϯճ਺
    ֶश࣌ؒΛߟ͑Ε͹
    LearningRate→0.03
    Iter→ 500 ~ 700
    Ͱ΋Α͔͕ͬͨstore_idຖ·ͨظؒʹ
    ΑͬͯऩଋͷλΠϛϯάͷζϨ͕͢
    ͜͠େ͖͔ͬͨͷͰࠓճ͸ίϯϖͱ
    ͍͏͜ͱ΋͋Γɺ
    LearningRate→0.01
    Iter→ 1200 & 1500(Blend)
    Λ࠾༻ͨ͠ɻ

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  18. ̓. Ϟσϧৄࡉ

    ʢ̐ʣ day-by-day Ϟσϧ
    ΍͸Γ1೔໨ͷϞσϧͷํ͕είΞ͕͔ͳ
    Γྑ͘ͳ͍ͬͯΔɻ
    ಛʹ̍ʙ̏೔໨ͷӨڹ͕େ͖͘ɺਫ਼౓Λ
    ٻΊΔͳΒ̎̔Ϟσϧ͸ॏཁͱײ͡Δɻ
    0.016

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  19. ̓. Ϟσϧৄࡉ

    • ݕূظؒ
    ʢݕূظؒ̍ʣ2016-04-25 ~ 2016-05-22 : score ໿0.53(Public LB)
    ʢݕূظؒ̎ʣ2016-03-28 ~ 2016-04-24 : score ໿0.51
    ʢݕূظؒ̏ʣ2016-02-29 ~ 2016-03-27 : score ໿0.60
    ʢςετظؒʣ2016-05-23 ~ 2016-06-19 : score 0.576 (Private LB)
    ɹɹ=>ݕূظؒʹؔͯ͠͸೔ຖʹΞΠςϜ͕࣍ʑʹ౤ೖ͞Ε͍ͯΔͨΊɺ·ͨ௚ۙʹτϨϯυ͕มΘͬͯɹ
    ɹɹɹɹ͍ΔՄೳੑ͕͋Δ͜ͱ͔Β΋ͳΔ΂͘௚લΛ࢖ͬͨɻ
    • ύϥϝʔλʔ
    store_idʹΑͬͯগ͠มߋɻ
    • ϝτϦοΫ
    ϊʔτϒοΫΛࢀߟʹ࡞੒ʢߦྻܭࢉΛ࢖༻͍ͯ͠ΔͨΊܭࢉ͕଎͍ʣ
    ɹ (https://www.kaggle.com/girmdshinsei/for-japanese-beginner-with-wrmsse-in-lgbm)
    • ࠶ؼతΞϓϩʔνɺ৐਺ͷ࢖༻ͳ͠
    • ޙॲཧ͸ͳ͠
    ʢ̑ʣ ͦͷଞ

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  20. ̓. Ϟσϧৄࡉ

    ʢ̍ʣ̑̌ˋ఺ͷࢉग़
    ̑̌ˋ఺͸ɺM5 - Accuracy ʹ͓͚Δ࠷ऴఏग़஋ͱ͢Δɻ
    ·ͨɺߟ͑ํͱͯ͠
    Accuracyͷ༧ଌϞσϧʹؔͯ͠
    ݕূظؒͷWRMSSEɹ㲈ɹςετظؒͷWRMSSE
    ͳΒ͹
    ݕূظؒͷޡࠩʢෆ࣮֬ੑʣɹ㲈ɹςετظؒͷޡࠩʢෆ࣮֬ੑʣ
    Accuracyͷ༧ଌϞσϧ͕ҰൠԽ͞Ε͍ͯ͹ɺAccuracyͷϞσϧ͸ͦͷ
    ··ෆ࣮֬ੑͱͯ͠࢖͑Δɻ

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  21. ̓. Ϟσϧৄࡉ

    ʢ̎ʣෆ࣮֬ྖҬͷࢉग़ํ๏ʢ̑̌ˋ఺Ҏ֎ͷࢉग़ʣ
    Accuracyͷ࠷ऴఏग़஋Λࢉग़ͨ͠ϞσϧΛ࢖༻ͯ͠
    ݕূظؒʹ͓͚Δޡࠩʹʛ࣮੷ʔ༧ଌ஋ʛΛͱΓɺޡࠩΛঢॱʹฒ΂Δ
    ࠓճ͸ݕূظؒΛ3ͭઃఆͨͨ͠Ί߹ܭ̎̔ˎ̏ʹ̔̐ݸͷޡ͕ࠩੜ͡Δɻ
    ex) diff = [0.5, 0.7, 1.4, 1.6, 1.7, 2.2, 2.6 ɾɾɾ 8.2, 8.5]
    ̔̐


    ̐̎ ̔̍
    άϥϑԽ
    ̑̒ ̔̐
    99.5% 0.5%ͷෆ࣮֬ੑ
    A
    B
    C
    D
    97.5% 2.5%ͷෆ࣮֬ੑ
    75.0% 25.0%ͷෆ࣮֬ੑ
    83.5% 16.5%ͷෆ࣮֬ੑ
    50.0%఺ ʔ D ʹ 0.5%఺
    50.0%఺ ʔ C ʹ 2.5%఺
    50.0%఺ ʔ B ʹ 16.5%఺
    50.0%఺ ʔ A ʹ 25.0%఺
    Accuracyͷ࠷ऴఏग़஋ ʹ 50.0%఺
    50.0%఺ ʴ A ʹ 75.0%఺
    50.0%఺ ʴ B ʹ 83.5%఺
    50.0%఺ ʴ C ʹ 97.5%఺
    50.0%఺ ʴ D ʹ 99.5%఺
    ঢॱԽͨ͠ޡࠩͷ͏ͪ̎̑ˋ఺ɺ̓̑ˋ఺ʹ͋ͨΔޡࠩʢ̐̎൪໨ͷޡࠩʣΛ̑̌ˋ఺͔Β૿ݮͤͨ͞΋ͷΛ̎̑ˋ఺ɺ̓̑ˋ఺ͱ
    ͠ɺଞ΋ಉ༷ʹల։͢Δɻ
    ※͜ͷޡ͕ࠩ͜ͷϞσϧʹ͓͚Δෆ࣮֬ੑͱͳΔ
    5SVF
    1SFE

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  22. ̓. Ϟσϧৄࡉ

    ࠓճݕূظؒΛ̎̔೔×̏Ͱߦͳ͕ͬͨ
    ຊདྷ֎Ε஋౳͕͋ͬͨ৔߹ͷճආ΋ߟ͑
    Δͱഒͷ̎̔೔×̒͸͋ͬͨํ͕Α͔ͬͨ
    ͱײ͡Δɻ
    ͕͔͔ͨͩ࣌ؒΓ͗͢ΔͨΊɺaccuracy
    ͷϞσϧΛΑΓ্ܰͨ͘͠Ͱਫ਼౓Λग़͢
    ͜ͱ͕͍Ζ͍ΖͳҙຯͰͷվળͷ༨஍ͱ
    ͳΔɻʢࠓޙͷ՝୊ʣ
    ࠓճίϯϖͰͷݕূظؒͷ༧ଌʹ͸
    earlystop=100, lr =0.08ͱ͠গ͠ߥ໨ͷઃ
    ఆͰߦ͍ͬͯΔɻʢaccuracyଆͷաֶ
    शɺֶशෆ଍ϦεΫରࡦɻʣ
    ʢ̏ʣ༧ଌ೔ຖͷෆ࣮֬ੑ
    ༧ଌ೔ʹԠͯ͡ෆ࣮֬ੑͷେ͖͞͸ҟͳΔɻ
    ࠓճAccuracyʹ͓͍ͯ͸೔ຖͷϞσϧ(̎̔Ϟσϧ)Λ࡞੒͓ͯ͠Γɺ
    ਫ਼౓͸̍೔໨ͷϞσϧͷํ͕̎̔೔໨ͷϞσϧΑΓ΋ྑ͘ͳΔɻ
    ͦͷͨΊෆ࣮֬ੑʹ͓͍ͯ΋̎̔ϞσϧͦΕͧΕʹ͓͚ΔޡࠩʢલϖʔδʣΛࢉग़͠ɺల։͢Δ͜ͱ͕๬·͍͠ɻ
    ※දͷAɺBɺCɺD͸લϖʔδͷͦΕΒͱಉ͡ҙຯ߹͍ɻ

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  23. ̔. ൓লͱ՝୊
    ֶश࣌ؒɿ
    ΋ͬͱݕূΛ͏·͘΍Ε͹ɺείΞΛ΄ͱΜͲམͱͣ͞ʹֶश࣌ؒΛେ෯ʹ୹͘Ͱ͖ͨͱࢥ͏ɻ
    ɾಛ௃ྔΛݮΒͯ͠ɺstore_id୯ҐͷϞσϧΛͳ͘͢ɻ
    ɾLearningRateͱIterationճ਺ͷௐ੔
    Etc
    Validationͷେࣄ͞ɿ
    ίϯϖং൫ɺPublicLBͷείΞʹؾΛऔΒΕ͗ͯ͢ɺޙ͔Βߟ͑Ε͹΍Δ΂͖Ͱͳ͍͜ͱʹ࣌ؒΛ͔͚ͯ͠
    ·ͬͨɻ͜ͷίϯϖͰValidationͷେ੾͞Λ௧ײͰ͖ͨ͜ͱ͸Α͔ͬͨɻ
    ๲େͳσʔλͷॲཧɿ
    ಛʹং൫͸ϝϞϦͷ੍ݶͷதͲ͏΍Δ͔Ͱ͔ͳΓ࿑ྗΛ࢖ͬͨɻػցֶशҎલʹ෼ࢄॲཧ΍σʔλܕͳͲ΋
    ͬͱษڧ͠ͳ͚Ε͹͍͚ͳ͍͜ͱ͕ͨ͘͞Μ͋Δɻ
    ධՁࢦඪͷཧղɿ
    ·ͣॳΊʹධՁࢦඪͷཧղΛਂΊͳ͚Ε͹͍͚ͳ͍͜ͱΛ௧ײͨ͠ɻ౰ॳ͸ධՁࢦඪͷཧղ͕ᐆດͷ··ਐ
    ΜͰ͍ͨͨΊɺ΍Δ΂͖Ͱͳ͍͜ͱΛଟ͘΍͍ͬͯͨɻධՁࢦඪʹΑͬͯ࡞Δ΂͖Ϟσϧ͕େ͖͘ҟͳΔ͜ͱ
    ͕Θ͔ͬͨɻ

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