In this study, we compared the prediction performances according to the bias and dispersion of temperature using ensemble machine learning. Ensemble machine learning is meta-algorithm that combines several base learners into one prediction model in order to improve prediction. Multiple linear regression, ridge regression, LASSO (Least Absolute Shrinkage and Selection Operator; Tibshirani, 1996) and nonnegative ride and LASSO were used as base learners. Super learner (van der Lann et al ., 1997) was used to produce one optimal predictive model. The simulation and real data for temperature were used to compare the prediction skill of machine learning. The results showed that the prediction performances were different according to the characteristics of bias and dispersion and the prediction error was more improved in temperature with bias compared to dispersion. Also, ensemble machine learning method showed similar prediction performances in comparison to the base learners and showed better prediction skills than the ensemble mean.

Abstract
1. 서론
2. 연구 자료
    1) 실제 자료
    2) 모의자료
3. 기계학습 방법
    1) 다중선형회귀와 양의 회귀 계수를 갖는 다중선형회귀
    2) 능형 회귀
    3) LASSO
    4) 양의 회귀 계수를 갖는 능형 회귀와 LASSO
    5) 앙상블 기계학습(Ensemble machine learning)
4. 연구 결과
    1) 실제 자료
    2) 모의자료
5. 결론
References

• 황유선(공주대학교 응용수학과) | Yuseon Hwang (Department of Applied Mathematics, Kongju National University)
• 김찬수(공주대학교 응용수학과) | Chansoo Kim (Department of Applied Mathematics, Kongju National University) Correspondence