Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
ロジスティック回帰 Part 2 - 係数、オッズ比、平均限界効果
Search
Kan Nishida
September 26, 2019
Science
0
1.3k
ロジスティック回帰 Part 2 - 係数、オッズ比、平均限界効果
Kan Nishida
September 26, 2019
Tweet
Share
More Decks by Kan Nishida
See All by Kan Nishida
Seminar #52 - Introduction to Exploratory Server
kanaugust
0
320
Exploratory セミナー #61 政府のオープンデータ e-Statの活用
kanaugust
0
1.1k
Exploratory セミナー #60 時系列データの加工、可視化、分析手法の紹介
kanaugust
0
1.1k
Seminar #51 - Machine Learning - How Variable Importance Works
kanaugust
0
640
Exploratory セミナー #59 テキストデータの加工
kanaugust
0
650
Seminar #50 - Salesforce Data, Clean, Visualize, Analyze, & Dashboard
kanaugust
1
370
Exploratory セミナー #58 Exploratory x Salesforce
kanaugust
0
350
Exploratory Seminar #49 - Introduction to Dashboard Cycle with Exploratory
kanaugust
0
360
Seminar #48 - Introduction to Exploratory v6.6
kanaugust
0
330
Other Decks in Science
See All in Science
Hakonwa-Quaternion
hiranabe
1
110
05_山中真也_室蘭工業大学大学院工学研究科教授_だてプロの挑戦.pdf
sip3ristex
0
520
機械学習 - SVM
trycycle
PRO
1
860
データベース01: データベースを使わない世界
trycycle
PRO
1
670
ウェブ・ソーシャルメディア論文読み会 第25回: Differences in misinformation sharing can lead to politically asymmetric sanctions (Nature, 2024)
hkefka385
0
120
データベース06: SQL (3/3) 副問い合わせ
trycycle
PRO
1
550
Agent開発フレームワークのOverviewとW&B Weaveとのインテグレーション
siyoo
0
280
機械学習 - 授業概要
trycycle
PRO
0
210
生成AIと学ぶPythonデータ分析再入門-Pythonによるクラスタリング・可視化をサクサク実施-
datascientistsociety
PRO
4
1.6k
CV_5_3dVision
hachama
0
140
研究って何だっけ / What is Research?
ks91
PRO
1
100
深層学習を用いた根菜類の個数カウントによる収量推定法の開発
kentaitakura
0
160
Featured
See All Featured
JavaScript: Past, Present, and Future - NDC Porto 2020
reverentgeek
50
5.5k
Building a Modern Day E-commerce SEO Strategy
aleyda
42
7.4k
Art, The Web, and Tiny UX
lynnandtonic
299
21k
RailsConf & Balkan Ruby 2019: The Past, Present, and Future of Rails at GitHub
eileencodes
138
34k
Building an army of robots
kneath
306
45k
4 Signs Your Business is Dying
shpigford
184
22k
BBQ
matthewcrist
89
9.7k
Save Time (by Creating Custom Rails Generators)
garrettdimon
PRO
31
1.3k
Intergalactic Javascript Robots from Outer Space
tanoku
271
27k
For a Future-Friendly Web
brad_frost
179
9.8k
Facilitating Awesome Meetings
lara
54
6.4k
The Web Performance Landscape in 2024 [PerfNow 2024]
tammyeverts
8
700
Transcript
ϩδεςΟοΫճؼ Part 2 ɺΦοζൺɺฏۉݶքޮՌ Exploratory Seminar #20
EXPLORATORY
3 εϐʔΧʔ ా צҰ CEO EXPLORATORY ུྺ 2016ɺσʔλαΠΤϯεͷຽओԽͷͨΊɺExploratory, Inc Λ
্ཱͪ͛Δɻ Exploratory, Inc.ͰCEOΛΊΔ͔ͨΘΒɺσʔλαΠΤϯεɾ ϒʔτΩϟϯϓɾτϨʔχϯάͳͲΛ௨ͯ͠γϦίϯόϨʔͰ ߦΘΕ͍ͯΔ࠷ઌͷσʔλαΠΤϯεͷීٴͱڭҭʹऔΓ Ήɻ ถΦϥΫϧຊࣾͰɺ16ʹΘͨΓσʔλαΠΤϯεͷ։ൃνʔ ϜΛ͍ɺػցֶशɺϏοάɾσʔλɺϏδωεɾΠϯςϦδΣ ϯεɺσʔλϕʔεʹؔ͢Δଟ͘ͷΛੈʹૹΓग़ͨ͠ɻ @KanAugust
Vision ΑΓΑ͍ҙࢥܾఆΛ͢ΔͨΊʹ σʔλΛ͏͜ͱ͕ͨΓલʹͳΔ
Mission σʔλαΠΤϯεͷຽओԽ
6 ୈ̏ͷ σʔλαΠΤϯεɺAIɺػցֶश౷ܭֶऀɺ։ൃऀͷͨΊ͚ͩͷͷͰ͋Γ·ͤΜɻ σʔλʹڵຯͷ͋ΔਓͳΒ୭͕ੈքͰ࠷ઌͷΞϧΰϦζϜΛͬͯ ϏδωεσʔλΛ؆୯ʹੳͰ͖Δ͖Ͱ͢ɻ Exploratory͕ͦ͏ͨ͠ੈքΛՄೳʹ͠·͢ɻ
ୈ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
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
ϩδεςΟοΫճؼ Part 2 ɺΦοζൺɺฏۉݶքޮՌ Exploratory Seminar #20
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
USͷͪΌΜσʔλ
ڵຯͷର ΧςΰϦʔ/ೋ߲ 12 ΧςΰϦʔ/ଟ߲
• ͷྸ͍͔ͭ͘ɺͷྸΛͱʹ༧ଌ͍ͨ͠ɻ • 35ࡀΑΓ্ͳͷ͔ɺͷྸΛͱʹ༧ଌ͍ͨ͠ɻ
• ͷྸ͍͔ͭ͘ɺͷྸΛͱʹ༧ଌ͍ͨ͠ɻ • 35ࡀΑΓ্ͳͷ͔ɺͷྸΛͱʹ༧ଌ͍ͨ͠ɻ
ڵຯͷର ΧςΰϦʔ/ೋ߲ 15 ΧςΰϦʔ/ଟ߲
ઢܗճؼ
17 Father_Age = a * Mother_Age + b ʢ͖ʣ ย
ઢܗճؼͷϞσϧʢܭࢉࣜʣ
18 Father_Age = a * Mother_Age + b ʢ͖ʣ ย
ͱยΛௐઅ͢Δ͜ͱͰ࣮σʔλͱ Ϛον͢ΔΑ͏ͳઢ͕ඳ͚Δɻ
19 ʢ͖ʣ ย
20 Father_Age = 0.87 * Mother_Age + 6.28 ʢ͖ʣ ย
ઢܗճؼͷϞσϧʢܭࢉࣜʣ
None
ͷྸ ͷྸ ͷྸ͕1্͕Δͱɺͷྸ0.87্͕Δɻ
ͷྸ ͷྸ ઢܗճؼͷϞσϧ࣮σʔλͱϑΟοτ͢ΔΑ͏ʹ࡞ΒΕΔɻ
• ͷྸ͍͔ͭ͘ɺͷྸΛͱʹ༧ଌ͍ͨ͠ɻ • 35ࡀΑΓ্ͳͷ͔ɺͷྸΛͱʹ༧ଌ͍ͨ͠ɻ
ڵຯͷର ΧςΰϦʔ/ೋ߲ 25 ΧςΰϦʔ/ଟ߲
• ͜ͷϢʔβʔίϯόʔτ͢Δ͔ʁ • ͜ͷऔҾෆਖ਼͔ʁ • ͜ͷैۀһΊΔ͔ʁ • ͜ͷͪΌΜະख़ࣇͰੜ·ΕΔ͔ʁ ೋ߲ͷ࣭
27 ͕35Ҏ্ͷ֬ = logistic(a * Mother_Age + b) ʢ͖ʣ ย
ϩδεςΟοΫճؼͷϞσϧʢܭࢉࣜʣ
28 ͕35Ҏ্ͷ֬ = logistic(a * Mother_Age + b) ʢ͖ʣ ย
ͱยΛௐઅ͢Δ͜ͱͰ࣮σʔλͱ Ϛον͢ΔΑ͏ͳۂઢ͕ඳ͚Δɻ
࣮σʔλ
දܭࢉͷʮׂ߹ʢˋ of ߹ܭʣʯΛͬͯ TRUE/FALSEͷׂ߹Λදࣔ͢Δɻ
ͷྸ͝ͱͷTRUE/FALSEͷׂ߹
ຌྫͷதͷFALSEΛΫϦοΫͯ͠ɺFALSEͷ෦ͷόʔΛফ͢ɻ
ଞʹʢͬͱ؆୯ʹʣಉ͡Α͏ͳ νϟʔτΛඳ͘ํ๏͕͋Δɻ
Y࣠ʹϩδΧϧܕͷྻΛબͼʮ% of TRUEʯͷܭࢉΛબͿɻ
ϥΠϯνϟʔτʹม͑ͯΈΔɻ
͜ͷ࣮σʔλʹϑΟοτ͢ΔϩδεςΟοΫۂઢΛग़͍ͨ͠ɻ
37 ͕35Ҏ্ͷ֬ = logistic(a * Mother_Age + b) ʢ͖ʣ ย
ͱยΛௐઅ͢Δ͜ͱͰ࣮σʔλͱ Ϛον͢ΔΑ͏ͳۂઢ͕ඳ͚Δɻ
38 ϩδεςΟοΫճؼͷϞσϧ
39 ͕35Ҏ্ͷ֬ = logistic(0.29 * Mother_Age - 10.12) ย
None
ϩδεςΟοΫճؼʹΑΔ༧ଌͷྻΛY࣠ʹׂΓͯɺ ʮฏۉʯͷܭࢉΛબͿɻ
ϩδεςΟοΫճؼʹΑΔ༧ଌ0͔Β1ͷؒͷͳͷͰɺ Y2࣠ʹׂΓͯΔɻ
࣮σʔλ Ϟσϧ (ϩδεςΟοΫۂઢ) ͍͍ײ͡Ͱ࣮σʔλʹϑΟοτͯ͠Δɻ
ͱ͜ΖͰɺ͜ͷۂઢɺͲ͏ղऍͨ͠Β͍͍ͷ͔ʁ P(Father > 35) = Logistic(0.29 * Mother_Age - 10.12)
45 ϩδεςΟοΫճؼ ༧ଌมͷӨڹͷղऍ
46 ϩδεςΟοΫճؼ • ʢCoefficientʣ • ΦοζൺʢOdds Ratioʣ • ฏۉݶքޮՌʢAverage Marginal
Effectʣ
47 ϩδεςΟοΫճؼ • ʢCoefficientʣ • ΦοζൺʢOdds Ratioʣ • ฏۉݶքޮՌʢAverage Marginal
Effectʣ
48 มͷࢦඪͱͯ͠ɺΛબ͢Δɻ
None
None
͕খ͍͞ͱɺ༧ଌม ͇ͷͷมԽ͕͈ͷ֬ ͷมԽʹ͋ͨ͑ΔӨڹ ͕খ͍͞ɻ 51 y = logistic(0.1 * x)
͕େ͖͍ͱɺ༧ଌม ͇ͷͷมԽ͕͈ͷ֬ ͷมԽʹ͋ͨ͑ΔӨڹ ͕େ͖͍ɻ 52 y = logistic(10 * x)
P(Father > 35) = Logistic(0.29 * Mother_Age - 10.12)
P(Father > 35) = Logistic(0.29 * Mother_Age - 10.12) Pr(Father
> 35) = Logit (0.29 * Mother_Age - 10.12) -1
Logit( P(Father > 35) ) = 0.29 * Mother_Age -
10.12 P(Father > 35) = Logistic(0.29 * Mother_Age - 10.12) P(Father > 35) = (0.29 * Mother_Age - 10.12) Logit -1
ϩδοτؔ֬ΛϩάɾΦοζม͢Δ Logit( P(y) ) = Log(Odds(y)) Logit( P(Father > 35)
) = 0.29 * Mother_Age - 10.12 Log(Odds(Father > 35)) = 0.29 * Mother_Age - 10.12
Log(Odds((Father > 35))) = 0.29 * 20 - 10.12 =
-4.32 ͕20 Log(Odds(Father > 35)) = 0.29 * Mother_Age - 10.12
Log(Odds((Father > 35))) = 0.29 * 20 - 10.12 =
-4.32 ͕21 Log(Odds((Father > 35))) = 0.29 * 21 - 10.12 = -4.03 ͕20 Log(Odds(Father > 35)) = 0.29 * Mother_Age - 10.12
Log(Odds((Father > 35))) = 0.29 * 20 - 10.12 =
-4.32 ͕21 Log(Odds((Father > 35))) = 0.29 * 21 - 10.12 = -4.03 ͕20 Log(Odds(Father > 35)) = 0.29 * Mother_Age - 10.12 0.29 ࠩ ͷྸ͕1ࡀ্͕Δͱɺ͕35ࡀҎ্Ͱ͋Δ ϩάɾΦοζ͕0.29্͕Δɻ
ϩάɾΦοζͬͯԿ͚ͩͬʁ
͏গ͠ਓؒతͳࢦඪ͕͋Δɻ
62 ϩδεςΟοΫճؼ • ʢCoefficientʣ • ΦοζൺʢOdds Ratioʣ • ฏۉݶքޮՌʢAverage Marginal
Effectʣ
None
64 Φοζൺ (Coefficient) ʹࢦؔ(logͷٯ)Λద༻ͨ͠ɻ Φοζൺ = exp()
65 ͕35Ҏ্ͷ֬ = logistic(a * Mother_Age + b) ʢ͖ʣ ย
ͱยΛௐઅ͢Δ͜ͱͰ࣮σʔλͱ Ϛον͢ΔΑ͏ͳۂઢ͕ඳ͚Δɻ
66 ϩδεςΟοΫճؼͷϞσϧ
67 ͕35Ҏ্ͷ֬ = logistic(0.29 * Mother_Age - 10.12) ย
None
֬ (Father > 35) ͷྸ
ϩδεςΟοΫۂઢ
ϩδεςΟοΫۂઢ͔ΒΦοζΛܭࢉͯ͠ΈΔɻ
72 Φοζ Φοζ = TRUEͷ֬ / FALSEͷ֬
73 ૣ࢈ʹͳΔΦοζ Φοζ = TRUEͷ֬ / FALSEͷ֬ ૣ࢈ʹͳΔ͕֬10% ૣ࢈ʹͳΒͳ͍͕֬90% 10
/ 90 = 0.1111…
74 50% 50% 100% 0% mother_age(ͷྸ) 34 When Mother is
34, what is the odds of Father being older than 35?
75 Φοζ 1 50% 50% 50/50 100% 0% mother_age(ͷྸ) 34
76 Φοζ 1 50% 50% 50/50 34 mother_age(ͷྸ) 100% 0%
77 1 50% 50% 66.7/33.3 2 33.3% 66.7% 34 35
Φοζ mother_age(ͷྸ) 100% 0%
78 1 50% 50% 80/20 2 33.3% 66.7% 34 35
20% 80% 36 4 Φοζ mother_age(ͷྸ) 100% 0%
79 1 50% 50% 88.9/11.1 33.3% 66.7% 34 35 20%
80% 36 11.1% 88.9% 37 2 4 8 Φοζ mother_age(ͷྸ) 100% 0%
80 มͷ͕1૿͑ΔͱɺΦοζԿഒʹͳΔ͔ɻ Φοζൺ (Odds Ratio)
81 TRUE FALSE 1 50% 50% 33.3% 66.7% 20% 80%
11.1% 88.9% 2 4 8 Φοζ 2x Φοζൺ mother_age(ͷྸ) 34 35 36 37
82 TRUE FALSE 1 50% 50% 33.3% 66.7% 20% 80%
11.1% 88.9% 2 4 8 Φοζ 2x Φοζൺ mother_age(ͷྸ)͕ 1্͕Δͱŋŋŋ TRUEͱͳΔΦοζ͕2ഒʹͳΔɻ mother_age(ͷྸ) 34 35 36 37
83 TRUE FALSE 1 50% 50% 33.3% 66.7% 20% 80%
11.1% 88.9% 2 4 8 Φοζ 2x Φοζൺ mother_age(ͷྸ) 34 35 36 37 Logistic Curve guarantee that this Odds Ratio is constant.
ม͕ΧςΰϦʔͷ࣌Ͳ͏ղऍ͢ΕΑ͍͔ɻ
༧ଌม͕ͷਓछʢΧςΰϦʔʣ
தࠃਓͷͷΦοζൺ0.5952ɻ
ΧςΰϦʔͷ࣌ϕʔεϨϕϧͱൺΔɻ
தࠃਓͷനਓͷʹൺͯΦοζൺ0.5952ߴ͍ɻ
தࠃਓͷനਓͷʹൺͯΦοζൺ0.5952ߴ͍ɻ ʁʁʁ
ϐϘοτςʔϒϧΛ࡞ͬͯߟ͑ͯΈΔɻ
தࠃਓ നਓ TRUE 296 39,221 FALSE 3,839 311,954
֬Λܭࢉ
தࠃਓ നਓ TRUE 296 39,221 FALSE 3,839 311,954 தࠃਓͷͷ࣌ʹTRUEʹͳΔ֬ʁ 296
(TRUE) / (296+3,839) (Total) = 0.072 (7.2%)
தࠃਓ നਓ TRUE 296 39,221 FALSE 3,839 311,954 39,221 (TRUE)
/ (39,221+311,954) (Total) = 0.112 (11.2%) നਓͷͷ࣌ʹTRUEʹͳΔ֬ʁ
ΦοζΛܭࢉ
96 Φοζ Φοζ = TRUEͷ֬ / FALSEͷ֬
தࠃਓ നਓ TRUE 296 39,221 FALSE 3,839 311,954 TRUEͷ֬: 296
/ (296+3,839) = 0.072 FALSEͷ֬: 1 - 0.072 = 0.928 Φοζ: 0.072 / 0.928 = 0.077 தࠃਓͷͷ࣌ʹTRUEʹͳΔΦοζʁ
தࠃਓ നਓ TRUE 296 39,221 FALSE 3,839 311,954 TRUEͷ֬: 39,221
/ (39,221 + 311,954) = 0.112 FALSEͷ֬: 1 - 0.112 = 0.888 Φοζ: 0.112 / 0.888 = 0.126 നਓͷͷ࣌ʹTRUEʹͳΔΦοζʁ
தࠃਓ നਓ TRUE 296 39,221 FALSE 3,839 311,954 0.126 0.077
Φοζ
தࠃਓ നਓ TRUE 296 39,221 FALSE 3,839 311,954 0.126 0.077
നਓʹൺͯதࠃਓ͕TRUEʹͳΔΦοζʁ Φοζ
தࠃਓ നਓ TRUE 296 39,221 FALSE 3,839 311,954 0.126 0.077
നਓʹൺͯதࠃਓ͕TRUEʹͳΔΦοζʁ 0.077 / 0.126 = 0.611 Φοζ Φοζൺ
தࠃਓ നਓ TRUE 296 39,221 FALSE 3,839 311,954 0.126 0.077
നਓʹൺͯதࠃਓ͕TRUEʹͳΔΦοζ0.611ഒʁ 0.077 / 0.126 = 0.611 Φοζ Φοζൺ
தࠃਓ നਓ TRUE 296 39,221 FALSE 3,839 311,954 0.126 0.077
നਓʹൺͯதࠃਓ͕TRUEʹͳΔΦοζ40ˋ͍ʁ 0.077 / 0.126 = 0.611 Φοζ Φοζൺ
The odds of Chinese Mothers having premature babies is 40%
less likely compared to White Mothers.
࣮͜͏͍͏දݱαΠΤϯεؔ࿈ͷ ൃදͰΑ͘Έ͔͚Δɻ
Source: More meat, more problems: Bacon may increase breast cancer
risk in Latinas. U of South Carolina News, Zen Vuong, March 3 2016 “ϕʔίϯΛຖ20άϥϜ΄Ͳ৯Δϥςϯܥͷঁੑ͕ೕ͕Μ ʹͳΔՄೳੑϕʔίϯΛ৯ͳ͍ϥςϯܥͷঁੑʹൺͯ 42ˋߴ͘ͳΔ͜ͱ͕ݚڀͷ݁ՌΘ͔ͬͨɻ”
Source: More meat, more problems: Bacon may increase breast cancer
risk in Latinas. U of South Carolina News, Zen Vuong, March 3 2016 “ϕʔίϯΛຖ20άϥϜ΄Ͳ৯Δϥςϯܥͷঁੑ͕ೕ͕Μ ʹͳΔΦοζϕʔίϯΛ৯ͳ͍ϥςϯܥͷঁੑʹൺͯ 1.42ഒͰ͋Δ͜ͱ͕ݚڀͷ݁ՌΘ͔ͬͨɻ”
108 ΦοζൺͷՄࢹԽ มͷࢦඪͱͯ͠ɺΦοζൺΛબ͢Δɻ
Odds Ratio = exp(Coefficient)
110 ͷྸ͕1ࡀ্͕Δͱɺ͕35ࡀҎ্Ͱ͋Δ Φοζ͕1.3ഒ্͕Δɻ
Φοζൺ͕Α͘ཧղग़དྷͳ͍ਓɻ ৺͠ͳ͍Ͱ͍ͩ͘͞ɻ
͏গ͠ײతͳࢦඪ͕͋Γ·͢ɻ
113 ϩδεςΟοΫճؼ • ʢCoefficientʣ • ΦοζൺʢOdds Ratioʣ • ฏۉݶքޮՌʢAverage Marginal
Effectʣ
114 ฏۉݶքޮՌ (Average Marginal Effect)
ฏۉݶքޮՌ (Average Marginal Effect) ม͕1্͕Δͱɺ͕֬ฏۉͯ͠ͲΕ্͚͕ͩΔͷ͔Λࣔ͢ɻ
ϩδεςΟοΫۂઢ
None
͋Δۃͷ͖ ݶքޮՌ
119 • ݶքޮՌɺ֤σʔλʹΑͬͯҧ͏ͷͰɺ͜ͷ·· ͰҰͭͷมͷࢦඪʹͳΒͳ͍ɻ • ͯ͢ͷσʔλʹ͍ͭͯݶքޮՌΛฏۉͯ͠Ұͭͷม ͷࢦඪʹͨ͠ͷ͕ฏۉݶքޮՌɻ ฏۉݶքޮՌ
ݶքޮՌ ͯ͢ͷσʔλͷݶքޮՌͷฏۉ
121 มͷࢦඪʹฏۉݶքޮՌΛબͿɻʢσϑΥϧτʣ
122 ฏۉݶքޮՌ ͷྸ͕1ࡀ্͕Δͱɺ͕̏̑ࡀҎ্ Ͱ͋Δ͕֬ฏۉͯ͠3%΄Ͳ͕͋Δɻ
มͷӨڹʹؔ͢Δ౷ܭςετ ʢԾઆݕఆʣ
PʢP Valueʣ
125 • ؼແԾઆɺʮ͜ͷมɺ࣮༧ଌ͍ͨ͠ͱؔͳ͍ɻʢͦ͏Έ ͑ΔͷۮવͰ͋Δʣʯ • P ɺؼແԾઆ͕ͳΓͨͭͱͨ͠ͱ͖ʹɺ࣮ࡍʹग़͍ͯΔͱಉఔ ͔ͦΕҎ্ʹมͱ݁Ռ͕ؔ࿈͍ͯ͠ΔΑ͏ʹݟ͑Δ֬ɻ • P͕
5%ҎԼͰ͋ΕɺؼແԾઆغ٫ग़དྷΔͷͰɺม݁Ռͱؔ ࿈͕͋Δͱߟ͑Δɻ PʢP Valueʣ
126 ༧ଌม͕1͚ͭͩͷ߹ΛΈ͖ͯͨɻ
Simple Logistic Regression P(y) = logistic(a * x + b)
ͪΌΜͷ͕1૿͑Δͱɺૣ࢈ʹͳΔΦοζ͕ 13ഒʹͳΔɻ Φοζൺͷ߹
ͪΌΜͷ͕1૿͑Δͱɺૣ࢈ʹͳΔ͕֬ฏۉͰ 23.67%্͕Δɻ ฏۉݶքޮՌͷ߹
ฏۉݶքޮՌͷ߹
131 ༧ଌม͕ෳͷ߹ɻ
Multiple Logistic Regression P(y) = logistic(a1 * x1 + a2
* x2 + b)
ෳͷྻΛ༧ଌมͱͯ͠બͿɻ
ଞͷมͷ͕ҰఆͰ͋Εɺ ͪΌΜͷ͕૿͑Δͱૣ࢈ʹͳΔΦοζ2.68ഒʹͳΔɻ Φοζൺͷ߹
ଞͷมͷ͕ҰఆͰ͋Εɺ ͪΌΜͷ͕૿͑Δͱૣ࢈ʹͳΔ֬ฏۉͰ7ˋ্͕Δɻ ฏۉݶքޮՌͷ߹
ฏۉݶքޮՌͷ߹
Q & A
None
• ϓϩάϥϛϯάͳ͠ RݴޠͷUIͰ͋ΔExploratoryΛੳπʔϧͱͯ͠༻͢ΔͨΊडߨதɺϏδωεͷ Λղܾ͢ΔͨΊʹඞཁͳσʔλαΠΤϯεͷख๏ͷशಘʹ100ˋूதͰ͖Δ • πʔϧͷ͍ํͰͳ͘ɺੳख๏ͷशಘ ݱͰ͑Δੳख๏ΛάϧʔϓԋशΛ௨࣮ͯ͠ࡍʹखΛಈ͔͠ͳ͕Βɺʹ͚ͭͯߦ͘ ͜ͱ͕Ͱ͖Δɻ • ࢥߟྗͱεΩϧͷशಘ
σʔλαΠΤϯεͷεΩϧशಘ͚ͩͰͳ͘ɺσʔλੳʹඞཁͳࢥߟྗशಘͰ͖Δ ಛ
࿈བྷઌ ϝʔϧ
[email protected]
ΣϒαΠτ https://ja.exploratory.io ϒʔτΩϟϯϓɾτϨʔχϯά https://ja.exploratory.io/training-jp Twitter @KanAugust
EXPLORATORY