When making predictions based on data, not all modeling techniques work equally well for all datasets. A new measure ’provides some statistical ’oomph’’ to help data scientists choose the best method for their task, says Tamara Broderick, an associate professor in EECS and a member of LIDS and IDSS, and whose team developed the tool. Credits : Photo: Jodi Hilton
When making predictions based on data, not all modeling techniques work equally well for all datasets. A new measure 'provides some statistical 'oomph'' to help data scientists choose the best method for their task, says Tamara Broderick, an associate professor in EECS and a member of LIDS and IDSS, and whose team developed the tool. Credits : Photo: Jodi Hilton A new measure can help scientists decide which estimation method to use when modeling a particular data problem. Close If a scientist wanted to forecast ocean currents to understand how pollution travels after an oil spill, she could use a common approach that looks at currents traveling between 10 and 200 kilometers. Or, she could choose a newer model that also includes shorter currents. This might be more accurate, but it could also require learning new software or running new computational experiments. How to know if it will be worth the time, cost, and effort to use the new method? A new approach developed by MIT researchers could help data scientists answer this question, whether they are looking at statistics on ocean currents, violent crime, children's reading ability, or any number of other types of datasets.
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