Extremely Randomized Tree (극단적 랜덤 트리)

  • Pierre Geurts, Extremely Randomized Tree, 2005
  • 랜덤 포레스트보다 랜덤화된 요소를 가지고 있는 트리
  • 트리 분기 시 컷포인트(cut-point)를 찾을때 이를 랜덤으로 결정
  • 분류문제는 투표로, 회귀문제는 평균으로 결과치를 산출.
  • 논문상 랜덤 포레스트보다 미세하기 앞서며, 노이즈 데이터에 대해서는 결과는 랜덤포레스트와 비슷하다는 제보.
Draw a random cut-point 라고 되어있는 부분이 핵심.
  • 사용자들의 이해도가 높지는 않아 단일로는 잘 쓰이지 않으며, 각종 대회등에서 앙상블 용으로 많이 이용.
  • 분기되는 지점을 랜덤으로 선택하기 때문에 훈련속도가 랜덤 포레스트보다 약 세배정도 빠른편으로, Scikit-Learn에 구현이 되어있음.
PST – Pruned Single Tree, TB – Tree Bagging, RS – Random Subspace, RF – Random Forests
  • 단일트리모델을 제외하고는, 앙상블 모델끼리의 유의할만한 차이는 보기 힘들다는 판단

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