So, there’s some sort of function mapping from (cities,widgets)->sales, plus randomness. In general, I would say use some standard machine learning technique, but if you know the function is linear you can do it directly.
So:
sales=constant x cityvalue x widgetvalue + noise
d sales/d cityvalue = constant x widgetvalue
d sales/d widgetvalue = constant x cityvalue
(all vectors)
So then you pick random starting values of cityvalue , widgetvalue, calculate the error and do gradient decent.
Or just plug
Error = sum((constant x cityvalue x widgetvalue—sales)^2)
Into an optimisation function, which will be slower but quicker to code.
So, there’s some sort of function mapping from (cities,widgets)->sales, plus randomness. In general, I would say use some standard machine learning technique, but if you know the function is linear you can do it directly.
So:
sales=constant x cityvalue x widgetvalue + noise
d sales/d cityvalue = constant x widgetvalue
d sales/d widgetvalue = constant x cityvalue
(all vectors)
So then you pick random starting values of cityvalue , widgetvalue, calculate the error and do gradient decent.
Or just plug
Error = sum((constant x cityvalue x widgetvalue—sales)^2)
Into an optimisation function, which will be slower but quicker to code.
Thank you! This seems like the conceptual shift I needed.