May 2018
Beginner to intermediate
672 pages
16h 54m
Korean
Content preview from 핸즈온 머신러닝: 사이킷런과 텐서플로를 활용한 머신러닝, 딥러닝 실무
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258
1
부
머신러닝
7.5.2
그래디언트 부스팅
인기가 높은 또 하나의 부스팅 알고리즘은 그래디언트 부스팅
Gradient
Boosting
30
입니다. 아다부스
트처럼 그래디언트 부스팅은 앙상블에 이전까지의 오차를 보정하도록 예측기를 순차적으로 추
가합니다. 하지만 아다부스트처럼 반복마다 샘플의 가중치를 수정하는 대신 이전 예측기가 만
든 잔여 오차
residual
error
에 새로운 예측기를 학습시킵니다.
결정 트리를 기반 예측기로 사용하는 간단한 회귀 문제를 풀어보겠습니다 (당연히 그래디언트
부스팅은 회귀 문제에도 아주 잘 작동합니다 ). 이를 그래디언트 트리 부스팅
Gradient
Tree
Boosting
또는 그래디언트 부스티드 회귀 트리
Gradient
Boosted
Regression
Tree
(
GBRT
)라고 합니다. 먼저
DecisionTreeRegressor
를 훈련 세트 (잡음이 섞인
2
차 곡선 형태의 훈련 세트)에 학습시켜
보겠습니다.
from sklearn
.
tree import DecisionTreeRegressor
tree
_
reg1
=
DecisionTreeRegressor
(
max
_
depth
=
2
)
tree
_
reg1
.
fit
(
X
,
y
)
이제 첫 번째 예측기에서 생긴 잔여 오차에 두 번째
DecisionTreeRegressor
를 훈련시킵니다.
y2
=
y
-
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.