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[Machine Learning] ํŠธ๋ฆฌ ๊ธฐ๋ฐ˜ ๋ฐฉ๋ฒ• (Decision trees)

ํŠธ๋ฆฌ ๊ธฐ๋ฐ˜ ๋ฐฉ๋ฒ•(tree-based methods) Predictor ๊ณต๊ฐ„(space) -> ์—ฌ๋Ÿฌ ๊ฐœ์˜ ๋‹จ์ˆœํ•˜๊ณ  ์ž‘์€ ๊ณต๊ฐ„์œผ๋กœ ๊ณ„์ธตํ™”(stratify), ๋‚˜๋ˆ„๋Š”(segment) ๋ฐฉ๋ฒ• => Predictor ๊ณต๊ฐ„์„ ๋‚˜๋ˆ„๊ธฐ ์œ„ํ•ด ์‚ฌ์šฉ๋˜๋Š” ๋ถ„ํ•  ๊ทœ์น™์ด, ๋งˆ์น˜ ๋‚˜๋ฌด๊ฐ€ ๊ฐ€์ง€๋ฅผ ์น˜๋Š” ๊ฒƒ๊ณผ ์œ ์‚ฌํ•˜์—ฌ decision tree ๋ฐฉ๋ฒ• ์žฅ์  : ๋‹จ์ˆœํ•ด์„œ ํ•ด์„ํ•˜๊ธฐ ์‰ฌ์›€ ๋‹จ์  : Decision tree ๋ฐฉ๋ฒ•์€ ๋ณดํ†ต ๋‹ค๋ฅธ ์ง€๋„ํ•™์Šต ๋ฐฉ๋ฒ•๋ณด๋‹ค ์„ฑ๋Šฅ์ด ์ข‹์ง€ ๋ชปํ•จ => ๋Œ€์•ˆ์œผ๋กœ ์—ฌ๋Ÿฌ ๊ฐœ์˜ ํŠธ๋ฆฌ๋ฅผ ๋งŒ๋“ค์–ด ์˜ˆ์ธก์„ฑ๋Šฅ์„ ๋†’์ด๋Š” ๋ฐฉ์‹์ธ bagging, random forests, boosting ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉ (๋‹จ, ์ด ๋ฐฉ๋ฒ•์€ ํ•ด์„๋ ฅ์ด ๋–จ์–ด์ง) Internal node(๋‚ด๋ถ€ ๋…ธ๋“œ) : ๊ธฐ์ค€์œผ๋กœ ๋น„๊ตํ•˜์—ฌ ์ขŒ์šฐ๋กœ ๋‚˜๋ˆ” Terminal node(ํ„ฐ๋ฏธ..