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
Tree Methods
Search
Sunmi Yoon
November 04, 2019
Technology
0
120
Tree Methods
Decision Tree, Random Forest를 dataitgirls3 학생들에게 가르치기 위해 만든 수업자료입니다.
Sunmi Yoon
November 04, 2019
Tweet
Share
More Decks by Sunmi Yoon
See All by Sunmi Yoon
데이터 분석가 채용 공고 읽는 방법
ysunmi0427
1
330
Deep down in classification 0.5 magic number
ysunmi0427
0
92
Confusion matrix
ysunmi0427
0
150
심슨의 역설
ysunmi0427
0
2.2k
회사는 어떤 사람을 데이터 분석가로 채용하고 싶어하는 것일까?
ysunmi0427
0
2.3k
Other Decks in Technology
See All in Technology
2時間で300+テーブルをデータ基盤に連携するためのAI活用 / FukuokaDataEngineer
sansan_randd
0
140
Vision Language Modelと自動運転AIの最前線_20250730
yuyamaguchi
3
1.2k
Segment Anything Modelの最新動向:SAM2とその発展系
tenten0727
0
600
ホリスティックテスティングの右側も大切にする 〜2つの[はか]る〜 / Holistic Testing: Right Side Matters
nihonbuson
PRO
0
650
Google Cloud で学ぶデータエンジニアリング入門 2025年版 #GoogleCloudNext / 20250805
kazaneya
PRO
19
4.3k
AI時代の経営、Bet AI Vision #BetAIDay
layerx
PRO
1
1.9k
o11yツールを乗り換えた話
tak0x00
2
550
Amazon S3 Vectorsは大規模ベクトル検索を低コスト化するサーバーレスなベクトルデータベースだ #jawsugsaga / S3 Vectors As A Serverless Vector Database
quiver
1
140
Findy Freelance 利用シーン別AI活用例
ness
0
390
Amazon Q と『音楽』-ゲーム音楽もAmazonQで作成してみた感想-
senseofunity129
0
130
마라톤 끝의 단거리 스퍼트: 2025년의 AI
inureyes
PRO
1
720
みんなのSRE 〜チーム全員でのSRE活動にするための4つの取り組み〜
kakehashi
PRO
2
140
Featured
See All Featured
Fight the Zombie Pattern Library - RWD Summit 2016
marcelosomers
234
17k
Typedesign – Prime Four
hannesfritz
42
2.7k
Chrome DevTools: State of the Union 2024 - Debugging React & Beyond
addyosmani
7
800
ピンチをチャンスに:未来をつくるプロダクトロードマップ #pmconf2020
aki_iinuma
126
53k
Let's Do A Bunch of Simple Stuff to Make Websites Faster
chriscoyier
507
140k
Intergalactic Javascript Robots from Outer Space
tanoku
272
27k
Documentation Writing (for coders)
carmenintech
73
5k
Embracing the Ebb and Flow
colly
86
4.8k
Responsive Adventures: Dirty Tricks From The Dark Corners of Front-End
smashingmag
251
21k
Sharpening the Axe: The Primacy of Toolmaking
bcantrill
44
2.4k
Measuring & Analyzing Core Web Vitals
bluesmoon
8
550
Designing for Performance
lara
610
69k
Transcript
Tree methods dataitgirls3 Instructor Sunmi Yoon
Decision Tree
Sex <= 0.5 gini = 0.473 samples = 891 value
= [549, 342] class = Survived Fare <= 26.269 gini = 0.306 samples = 577 value = [468, 109] class = Survived True Fare <= 48.2 gini = 0.383 samples = 314 value = [81, 233] class = Dead False gini = 0.226 samples = 415 value = [361, 54] class = Survived gini = 0.448 samples = 162 value = [107, 55] class = Survived gini = 0.447 samples = 225 value = [76, 149] class = Dead gini = 0.106 samples = 89 value = [5, 84] class = Dead
Sex <= 0.5 gini = 0.473 samples = 891 value
= [549, 342] class = Survived Fare <= 26.269 gini = 0.306 samples = 577 value = [468, 109] class = Survived True Fare <= 48.2 gini = 0.383 samples = 314 value = [81, 233] class = Dead False gini = 0.226 samples = 415 value = [361, 54] class = Survived gini = 0.448 samples = 162 value = [107, 55] class = Survived gini = 0.447 samples = 225 value = [76, 149] class = Dead gini = 0.106 samples = 89 value = [5, 84] class = Dead Root Node (ࡸܻ) Intermediate Node (о) Terminal Node, Leaf ()
Sex <= 0.5 gini = 0.473 samples = 891 value
= [549, 342] class = Survived Fare <= 26.269 gini = 0.306 samples = 577 value = [468, 109] class = Survived True Fare <= 48.2 gini = 0.383 samples = 314 value = [81, 233] class = Dead False gini = 0.226 samples = 415 value = [361, 54] class = Survived gini = 0.448 samples = 162 value = [107, 55] class = Survived gini = 0.447 samples = 225 value = [76, 149] class = Dead gini = 0.106 samples = 89 value = [5, 84] class = Dead അ ਤী ؘఠо ݻ ѐ ਤ೧ ח Ӓ ؘఠٜ যڃ ۄ߰ਸ оҊ ח
Sex <= 0.5 gini = 0.473 samples = 891 value
= [549, 342] class = Survived Fare <= 26.269 gini = 0.306 samples = 577 value = [468, 109] class = Survived True Fare <= 48.2 gini = 0.383 samples = 314 value = [81, 233] class = Dead False gini = 0.226 samples = 415 value = [361, 54] class = Survived gini = 0.448 samples = 162 value = [107, 55] class = Survived gini = 0.447 samples = 225 value = [76, 149] class = Dead gini = 0.106 samples = 89 value = [5, 84] class = Dead যڃ ӝળਵ۽ оӝܳ ೮ח (gini ژח entropy)
Sex <= 0.5 gini = 0.473 samples = 891 value
= [549, 342] class = Survived Fare <= 26.269 gini = 0.306 samples = 577 value = [468, 109] class = Survived True Fare <= 48.2 gini = 0.383 samples = 314 value = [81, 233] class = Dead False gini = 0.226 samples = 415 value = [361, 54] class = Survived gini = 0.448 samples = 162 value = [107, 55] class = Survived gini = 0.447 samples = 225 value = [76, 149] class = Dead gini = 0.106 samples = 89 value = [5, 84] class = Dead Terminal Nodeী بೠ ؘఠٜਸ যڌѱ ࠙ܨೡ Ѫੋ
sklearn Code
Impurity
Impurity ࢎѾաޖח Impurity (ࠛࣽب, ࠛഛपࢿ) ծইח ߑߨਵ۽ णפ. ࣽبо ૐоೞח
Ѫਸ فҊ Information gainۄҊ ೞӝب פ. য়ט ࢎѾաޖ ࠛࣽب ஏ ߑߨ , Gini Indexܳ ҕࠗפ.
Sex <= 0.5 gini = 0.473 samples = 891 value
= [549, 342] class = Survived Fare <= 26.269 gini = 0.306 samples = 577 value = [468, 109] class = Survived True Fare <= 48.2 gini = 0.383 samples = 314 value = [81, 233] class = Dead False gini = 0.226 samples = 415 value = [361, 54] class = Survived gini = 0.448 samples = 162 value = [107, 55] class = Survived gini = 0.447 samples = 225 value = [76, 149] class = Dead gini = 0.106 samples = 89 value = [5, 84] class = Dead G = d ∑ i=1 Ri ( 1 − m ∑ k=1 p2 ik) Step 1. gini = 0.473 ਸ ҅೧ যࠁࣁਃ Step 2. gini = 0.226 ਸ ҅೧ যࠁࣁਃ
https://imgur.com/n3MVwHW
Random Forest
ৈ۞ ܻٜਸ ‘ܰѱ’ ݅ٚ. https://www.researchgate.net/figure/Architecture-of-the-random-forest-model_fig1_301638643
https://community.alteryx.com/t5/Alteryx-Designer-Knowledge-Base/Seeing-the-Forest-for-the-Trees-An-Introduction-to-Random-Forest/ta-p/158062 bagging = bootstrap aggregating
Bagging ߓӦ(bagging) bootstrap aggregating ড۽, ࠗझە(bootstrap)ਸ ా೧ ઑӘঀ ܲ ള۲
ؘఠী ೧ ള۲ػ ӝୡ ࠙ܨӝ(base learner)ٜਸ Ѿ(aggregating)दఃח ߑߨ. ࠗझەۆ, য ള۲ ؘఠীࢲ ࠂਸ ೲਊೞৈ ਗ ؘఠࣇҗ э ӝ ؘఠࣇਸ ݅٘ח җਸ ݈ೠ. ߓӦਸ ా೧ ےؒ ನۨझܳ ള۲दఃח җ җ э ࣁ ױ҅۽ ೯ػ. 1. ࠗझە ߑߨਸ ా೧ Nѐ ള۲ ؘఠࣇਸ ࢤࢿೠ. 2. Nѐ ӝୡ ࠙ܨӝ(ܻ)ٜਸ ള۲दఅ. 3. ӝୡ ࠙ܨӝ(ܻ)ٜਸ ೞա ࠙ܨӝ(ےؒ ನۨझ)۽ Ѿೠ(ಣӐ ژח җ߈ࣻై ߑध ਊ). Wikipedia ےؒನۨझ > ߓӦਸ ਊೠ ನۨझ ҳࢿ
sklearn Code