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random forest 1

Random Forest ๊ฐ„.๋‹จ.๋ช….๋ฃŒ

Ensemble ์•™์ƒ๋ธ” ์—ฌ๋Ÿฌ ๊ฐœ์˜ ๋จธ์‹ ๋Ÿฌ๋‹ model์„ ์—ฐ๊ฒฐํ•˜์—ฌ ๊ฐ•๋ ฅํ•œ model์„ ๋งŒ๋“œ๋Š” ๊ธฐ๋ฒ• classifier/regression์— ์ „๋ถ€ ํšจ๊ณผ์  random forest์™€ gradient boosting์€ ๋‘˜๋‹ค model์„ ๊ตฌ์„ฑํ•˜๋Š” ๊ธฐ๋ณธ ์š”์†Œ๋กœ decision tree๋ฅผ ์‚ฌ์šฉํ•œ๋‹ค random forest ์กฐ๊ธˆ์”ฉ ๋‹ค ๋‹ค๋ฅธ ์—ฌ๋Ÿฌ decision tree์˜ ๋ฌถ์Œ ๋žœ๋ค ํฌ๋ ˆ์ŠคํŠธ์˜ ๋“ฑ์žฅ ๋ฐฐ๊ฒฝ : ๊ฐ๊ฐ์˜ tree๋Š” ๋น„๊ต์  ์˜ˆ์ธก์„ ์ž˜ ํ•  ์ˆ˜ ์žˆ์ง€๋งŒ, ๋ฐ์ดํ„ฐ์˜ ์ผ๋ถ€์— overfittingํ•˜๋Š” ๊ฒฝํ–ฅ์„ ๊ฐ€์ง ๋”ฐ๋ผ์„œ, ์ž˜ ์ž‘๋™ํ•˜์ง€๋งŒ ์„œ๋กœ ๋‹ค๋ฅธ ๋ฐฉํ–ฅ์œผ๋กœ overfitting๋œ tree๋ฅผ ๋งŽ์ด ๋งŒ๋“ค๊ณ  ๊ทธ ๊ฒฐ๊ณผ๋ฅผ ํ‰๊ท ๋‚ด๋ฉด overfitting์„ ์ค„์ผ ์ˆ˜ ์žˆ๋‹ค ์ด๋ ‡๊ฒŒ ํ•˜๋ฉด tree model์˜ ์˜ˆ์ธก ์„ฑ๋Šฅ์€ ์œ ์ง€ํ•˜๋˜ overf..

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