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RandomForest와 XGBoost를 활용한 한국어 텍스트 분류: 서울특별시 응답소 민원 데이터를 중심으로

Author
하지은, 신현철, 이준기
Journal Title
한국빅데이터학회 학회지
Publication Year
2017
Summary

This study compared the performance of RandomForest and XGBoost models for Korean text classification using 7 years of complaint data from the Seoul Response Center. The XGBoost model showed generally higher accuracy than the RandomForest model and maintained stable performance even after data sampling.

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