堆积
计算机科学
机器学习
逻辑回归
人工智能
系列(地层学)
核磁共振
生物
物理
古生物学
标识
DOI:10.1109/dsmp.2018.8478522
摘要
In this paper, we study the usage of stacking approach for building ensembles of machine learning models. The cases for time series forecasting and logistic regression have been considered. The results show that using stacking technics we can improve performance of predictive models in considered cases.
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