Make sure not to break the links when you paste the new copy!
Old:
TRAINSET evolved from a tool called SUMSarizer. SUMSarizer helps facilitate the application of ensemble machine learning tools to time series data. Most SUMSarizer users apply the tool to detect cooking events from temperature sensors called stove use monitoring systems (SUMS). SUMS are used to monitor cookstove adoption. TRAINSET was supported by the Implementation Science Network of the US National Institutes of Health. The development of SUMSarizer, which TRAINSET is based on, was supported by the Center for Effective Global Action (CEGA) and Innovations for Poverty Action (IPA). SUMSarizer is an open-source R package available on SUMSarizer's GitHub page.
New
TRAINSET evolved from a tool called SUMSarizer. SUMSarizer helps facilitate the application of ensemble machine learning tools to time series data. Most SUMSarizer users apply the tool to detect cooking events from temperature sensors called stove use monitoring systems (SUMS). SUMS are used to monitor cookstove adoption. The development of TRAINSET was funded by the NIH Clean Cooking Implementation Science Network with funding from the NIH Common Fund for Global Health. In addition to to the development of TRAINSET, NIH also supported further development of SUMSarizer. The original development of the first SUMSarizer was supported by the Center for Effective Global Action (CEGA) and Innovations for Poverty Action (IPA). SUMSarizer is an open-source R package available on SUMSarizer's GitHub page.
Make sure not to break the links when you paste the new copy!
Old: TRAINSET evolved from a tool called SUMSarizer. SUMSarizer helps facilitate the application of ensemble machine learning tools to time series data. Most SUMSarizer users apply the tool to detect cooking events from temperature sensors called stove use monitoring systems (SUMS). SUMS are used to monitor cookstove adoption. TRAINSET was supported by the Implementation Science Network of the US National Institutes of Health. The development of SUMSarizer, which TRAINSET is based on, was supported by the Center for Effective Global Action (CEGA) and Innovations for Poverty Action (IPA). SUMSarizer is an open-source R package available on SUMSarizer's GitHub page.
New TRAINSET evolved from a tool called SUMSarizer. SUMSarizer helps facilitate the application of ensemble machine learning tools to time series data. Most SUMSarizer users apply the tool to detect cooking events from temperature sensors called stove use monitoring systems (SUMS). SUMS are used to monitor cookstove adoption. The development of TRAINSET was funded by the NIH Clean Cooking Implementation Science Network with funding from the NIH Common Fund for Global Health. In addition to to the development of TRAINSET, NIH also supported further development of SUMSarizer. The original development of the first SUMSarizer was supported by the Center for Effective Global Action (CEGA) and Innovations for Poverty Action (IPA). SUMSarizer is an open-source R package available on SUMSarizer's GitHub page.