In my last two posts, I described a situation in which we wanted to roll out a new predictive model to the wet lab team to define the compound concentrations that would be used in upcoming experiments. When we left off, I suggested that there were two different approaches, depending on what you would do if the predictive model turns out to be junk. Today, I want to explore the first option - how we would proceed if a failed model meant we would scrap the project and move on to something else.
+1 "The data scientist will be the app"
The 17th, 18th, and 19th centuries were amazing in this regard. Knowledge progressed to some degree via correspondence and a few journals.