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Using a DEA–AutoML Approach to Track SDG Achievements
Each country needs to monitor progress on their Sustainable Development Goals (SDGs) to develop strategies that meet the expectations of the United Nations. Data envelope analysis (DEA) can help identify best practices for SDGs by setting goals to compete against. Automated machine learning (AutoML) simplifies machine learning for researchers who need less time and manpower to predict future situations. This work introduces an integrative method that integrates DEA and AutoML to assess anddoi:10.3390/su122310124 fatcat:ssnapjnopjgb5pw74oqzl5df5e