오, 훨씬 좋네요. 랜덤 포레스트는 매우 훌륭해 보입니다. 하지만 훈련 세트에 대한 점수가 검증
세트에 대한 점수보다 훨씬 낮으므로 이 모델도 여전히 훈련 세트에 과대적합되어 있습니다. 과
대적합을 해결하는 방법은 모델을 간단히 하거나, 제한을 하거나(즉, 규제), 더 많은 훈련 데
이터를 모으는 것입니다. 그러나 랜덤 포레스트를 더 깊이 들어가기 전에, 여러 종류의 머신러
닝 알고리즘으로 하이퍼파라미터 조정에 너무 많은 시간을 들이지 않으면서 다양한 모델(다양
한 커널의 서포트 벡터 머신, 신경망 등)을 시도해봐야 합니다. 가능성 있는
2
~
5
개 정도의 모
델을 선정하는 것이 목적입니다.
TIP
실험한 모델을 ...
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