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
ANNとナイーブベイズを使った雑な野球選手の成績予測 / Baseball player p...
Search
Shinichi Nakagawa
PRO
July 22, 2020
Research
3.3k
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
ANNとナイーブベイズを使った雑な野球選手の成績予測 / Baseball player performance prediction with Python
PyCon JP 2020で話す予定の話のダイジェストです.
kawasaki.rb #86 での練習試合.
#Python #DataScience #MLB #Baseball
Shinichi Nakagawa
PRO
July 22, 2020
More Decks by Shinichi Nakagawa
See All by Shinichi Nakagawa
LLMの出力を"いい感じに"する技術 / Taming LLM Output: AI Agent Design Patterns with FastAPI
shinyorke
PRO
4
1.8k
野球解説AI Agentを開発してみた - 2026/02/27 LayerX社内LT会資料
shinyorke
PRO
0
700
WBCの解説は生成AIにやらせよう - 生成AIで野球解説者AI Agentを実現する / Baseball Commentator AI Agent for Gemini
shinyorke
PRO
1
690
自らを強いエンジニアにするための3つの習慣 2025/ Fitter happier more productive
shinyorke
PRO
0
320
生成AI時代におけるSREの進化とキャリア戦略 / Building an Embedded SRE team and my career
shinyorke
PRO
0
190
生成AIを活用した野球データ分析 - メジャーリーグ編 / Baseball Analytics for Gen AI
shinyorke
PRO
1
6.7k
ゼロから始めるSREの事業貢献 - 生成AI時代のSRE成長戦略と実践 / Starting SRE from Day One
shinyorke
PRO
3
9.6k
AI・LLM事業部のSREとタスクの自動運転
shinyorke
PRO
0
590
実践Dash - 手を抜きながら本気で作るデータApplicationの基本と応用 / Dash for Python and Baseball
shinyorke
PRO
2
4.9k
Other Decks in Research
See All in Research
Harness Engineering and Al Agent
kzinmr
3
1.9k
RS-Agent: Automating Remote Sensing Tasks through Intelligent Agent
satai
3
530
Ghost in the 7‑Zip: The Shadow of Residential Proxies Creeping into Your Life
nttcom
0
2.1k
Sleuthcon Keynote - How Cybercriminals (ab)use AI
fr0gger
0
320
論文紹介:Doc-to-LoRA: Learning to Instantly Internalize Contexts
yukako_nakano
0
150
AIエージェント時代のLLM-jpモデルのあるべき姿
k141303
0
620
PGDM: Physically Guided Diffusion Model for L Downscaling
satai
3
460
[最先端NLP勉強会2026] Agentic Rubrics as Contextual Verifiers for SWE Agents
rfujii
1
340
全国町字単位空き家率推定データver1.0データ仕様
microbaseinc
0
220
クラウド・AI 時代の研究開発 DX / R&D Digital Transformation
hariby
0
130
typst の使い方:言語学を研究する学生のために
gitomochang
0
580
Vector Map as Language: Toward Unified Remote Sensing Vector Mapping
satai
3
260
Featured
See All Featured
Introduction to Domain-Driven Design and Collaborative software design
baasie
1
970
The AI Revolution Will Not Be Monopolized: How open-source beats economies of scale, even for LLMs
inesmontani
PRO
3
3.7k
Applied NLP in the Age of Generative AI
inesmontani
PRO
4
2.4k
WENDY [Excerpt]
tessaabrams
12
39k
Testing 201, or: Great Expectations
jmmastey
46
8.3k
Designing for humans not robots
tammielis
254
26k
Design in an AI World
tapps
1
300
The Web Performance Landscape in 2024 [PerfNow 2024]
tammyeverts
12
1.3k
Building an army of robots
kneath
306
46k
Optimizing for Happiness
mojombo
378
71k
Rebuilding a faster, lazier Slack
samanthasiow
85
9.6k
Color Theory Basics | Prateek | Gurzu
gurzu
0
460
Transcript
ٿબखͷ༧ଌϞσϧΛ ͍͍ײ͡ʹ࡞ͬͯΈͨVer 1.0 Shinichi Nakagawa (@shinyorke) kawasaki.rb #86 7पͪΐͬͱLTେձ
Who am I ? • Shinichi Nakagawa(@shinyorke) • JX௨৴ࣾγχΞɾΤϯδχΞ •
࠷ۙͣͬͱσʔλج൫ɾσʔλੳ͍ͯ͠ΔϚϯ • ຊདྷٿσʔλαΠΤϯεʹڧ͍ਓ • ٕज़ސ͡Ί·ͨ͠
kwsk.pyҊ݅Ͱ͢ :bow: PyCon JPʹ2ͿΓ6ճͷ⽁Λ͢Δ͜ͱʹͳΓ·ͯ͠. ٱ͠ͿΓʹนଧͪʹͬͯ·͍Γ·͓͖ͨ͠߹͍͍ͩ͘͞⽁
ʲਤʳࠓճ͖ͬͯͨ͜ͱ ຊ֨తͳ։ൃ4݄͔Β, ࠷ޙͷλεΫ͕௨ͬͨͷ͕͍ͭ࠷ۙ اըɾߏؚΊΔͱ࣮͍ۙϓϩδΣΫτͩͬͨΓ
None
σʔληοτ࡞ɾಛྔநग़ • ϝδϟʔϦʔάͷσʔλʮSean Lahmanʯʮretrosheetʯ ͜ΕΒΛͯ͢BigQueryʹimport • CSV͔Βςʔϒϧ࡞ • ػցֶशλεΫʹඞཁͳಛྔΛ۪ʹࢉग़
ػցֶशλεΫͦͷᶃ ʮࣅ͍ͯΔબखΫϥελΛ࡞Δʯ
कඋҐஔɾͷงғؾͰΫϥελϦϯά • ࡶʹݴ͏ͱ, ʮ˓˓ͬΆ͍બखϥϯΩϯάʯΛ࡞Δ • ྫ͑ࡔຊ༐ਓʢڊਓʣͬΆ͍બखʁͱݴΘΕͨΒ, ʮकඋҐஔ͕γϣʔτʯʮৗʹ3ׂ20ຊྥଧଧͭʯ ͱ͔ͦΜͳײ͡. γϣʔτͰ͋Δ͜ͱϚετ, ͋ͱଧܸ࣍ୈ.
• ଧܸ͓ΑͼҰ෦ͷकඋࢦඪΛͬͯϢʔΫϦουڑΛ ٻΊͯ૯ΓͰ֤બखͷʮͦΕͬΆ͍ϥϯΩϯάʯΛ࡞Εͦ͏.
ۙࣅ࠷ۙ୳ࡧʢANNʣͰͬͯΈͨ • kNNͱ͔k-meansͱ͔Γํ৭ʑ͚͋ͬͨͲANNͰͬͨ݁Ռ ͕͍͖ͳΓ͍͍ײͩͬͨ͡ͷͰ͜Εʹͨ͠. • ANNͷλεΫAnnoy͍ͬͯ͏ϥΠϒϥϦͰര։ൃ. • ϝδϟʔϦʔΨʔ19,000ਓͷσʔλͰͬͨΒ͍͍ײ͡ʹ.
ίʔυʢҰ෦ൈਮʣ˞ಛྔൿີ ֶश͔ΒϞσϧอଘͨͬͨ͜Ε͚ͩ. σʔλେ͖͘ͳ͍ͷͰඵͰऴΘΓ·ͨ͠.
ϚοτɾνϟοϓϚϯʢMLBएखࡾྥखʣʹ͍ۙબख ٬؍తͳσʔλ͔Β, ϑΝϯͱͯͬͯ͠Δͱͯ͠. ͍ۙબख͕ͪΌΜͱू·Γ·ͨ͠, શһࡾྥखͰଧܸͰ݁Ռग़ͤΔϚϯͳͷͰจ۟ͳ͠ʂ
ࣅ͍ͯΔબखूΊʹޭ ʢଞͷϙδγϣϯ͍͍ײͩͬͨ͡ʣ ޙ͔ͬ͜ΒߋʹΧςΰϦʔྨͯ͠ ʮະདྷͷΛ࡞ΓࠐΉʯ ࣄ͕Ͱ͖ͨΒʂ
ػցֶशλεΫͦͷᶄ ʮಉ͡ΧςΰϦͷબखΛݟ͚ͭΔʯ
φΠʔϒϕΠζʹΑΔΧςΰϦʔ͚ • ࣗવݴޠॲཧͷྨλεΫΈ͍ͨͳղ͖ํͰͬͯΈͨ. • ީิʮφΠʔϒϕΠζʯʮϥϯμϜϑΥϨετʯ͋ͨΓ. ࠓճφΠʔϒϕΠζͰͬͨ. • ٿʹ͓͚Δ౷߹తͳೳྗࢦඪʮOPSʯΛ͝ͱͷΧςΰϦʔʹ͚, ͍͔ͭ͘ͷଧܸࢦඪΛϕΫτϧʹ࣮ͯ͠ࢪ. •
࣮ී௨ʹscikit-learnͱPandasͰΓ·ͨ͠.
ͬͨ͜ͱʢཁʣɹ˞ࡶʹॻ͍ͯ·͢ • ֶशσʔλ • ༧ଌ͍ͨ͠બखʹࣅͨબख50ਓͷΛϐοΫΞοϓ • ಛྔൿີͰ͕͢…ී௨ͷଧܸʹӅ͠ຯগʑ • ༧ଌσʔλ •
༧ଌ͍ͨ͠બखͷಛྔ • ݁Ռͷϥϕϧσʔλ • OPSΛ5ஈ֊ͷΧςΰϦʹͨ͠ͷ(1ʙ5) • ্هͰࢦఆͨ͠ΧςΰϦʹଐ͢Δબखͷྸผฏۉ͔ΒͦΕͬΆ͍Λग़͢
༧ଌͱҰॹʹݟͯΈ·͠ΐ͏͔.
ϚοτɾνϟοϓϚϯʢݱ࣮ͷʣ 24ʙ26ࡀʢڈ·Ͱʣͷ. ༧ଌ͍ͨ͠ͷ27ʙ29ࡀͷ.
ϚοτɾνϟοϓϚϯʢ༧ଌ͖ʣ 27ࡀҎ߱ͷΛ༧ଌͨ݁͠ՌΛؚΊͨάϥϑ.
None
ग़͖ͯͨ݁ՌΛ͡Δͱ… • ൺֱత, ݱ࣮ʹଈͯ͠ΔͬΆ͍݁ՌʹͳΓ·ͨ͠. • ʮ28ࡀͷ͕Maxʯʮ29ࡀ͔ΒԼ͕ͬͯΔʯͨΓ͕ϦΞϧ. ※ΞεϦʔτͷମత࠷ߴை26ʙ28ࡀͱݴΘΕ͍ͯ·͢ • ͱ͍͑28ࡀͷຊྥଧ্͕͕ͬͯΔͷ, ͳΜ͔ո͍͠.
͓ͦΒ͘୭͔ͷʹҾͬுΒΕ͍ͯΔ.
Γ͠ɾվળϙΠϯτ • ࠷ޙͷྨ, ϕΠζҎ֎ࢼ͍ͨ͠. • ʮ28ࡀΛʹਰ͑ΔʯϙδγϣϯʹΑͬͯҧ͏આ͋Δ. ͷͰʮ্ͷʯΛٻΊΔλεΫ͕͍͍͔͋ͬͯ. • 2020ͷϝδϟʔϦʔάྫͷͷࢼ߹ͳͷͰ, ༧ଌͦ͜ʹ߹Θ͍ͤͨʢ2ͰׂͬͯऴΘΔʁwʣ
• ͱ͍͏ͷ͕PyCon JP 2020·ͰʹͰ͖ͯΔͣʢVer. 25ʮTsurageʯͰʣ
ଓ͖PyCon JP 2020Ͱʂ #͓͠·͍ #͝ਗ਼ௌ͋Γ͕ͱ͏͍͟͝·ͨ͠ Shinichi Nakagawa(Twitter/Facebook/etc… @shinyorke)