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NTIS 바로가기Journal of Korean Tunnelling and Underground Space Association = 한국터널지하공간학회논문집, v.22 no.5, 2020년, pp.501 - 513
이기준 (한국과학기술원 건설및환경공학과) , 류희환 (한국전력연구원) , 권태혁 (한국과학기술원 건설및환경공학과)
Cutter cutting tests for the cutter placement in the cutter head are being conducted through various studies. Although the cutter spacing at the minimum specific energy is mainly reflected in the cutter head design, since the optimum cutter spacing at the same cutter penetration depth varies dependi...
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핵심어 | 질문 | 논문에서 추출한 답변 |
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의사결정나무 학습법은 무슨 기법인가? | 의사결정나무 학습법(Decision tree learning)은 데이터를 각 질문에 따라 차례차례 분류하는 기법이다(Fig. 2). | |
랜덤 포레스트 알고리즘은 어떤 방식인가? | 랜덤 포레스트(Random forest) 알고리즘은 주어진 데이터 세트에서 무작위로 n개의 데이터를 샘플링해서 여러 개의 의사결정나무를 만든 후 각각의 의사결정나무의 예측결과를 토대로 다수결에 의해 최종 예측을 결정하는 방식이다(Fig. 3). | |
랜덤 포레스트에서 생성되는 의사결정나무수가 많을수록 무엇이 요구되는가? | 랜덤 포레스트에서 생성되는 의사결정나무수가 많을수록 다수결에 의한 예측 결과의 품질이 높아지게 된다. 하지만, 생성되는 의사결정나무수가 많을수록 분석에 필요한 공간이 더 늘어나게 되며 분석을 수행하는 장비의 더 높은 성능이 요구된다. 본 연구에서는 1,000개의 의사 결정나무를 생성하여 분석하였다. |
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