Heterogeneous Pareto Front

What happens if you take the automatic model type selection algorithm (based on heterogeneous evolution) implemented in the SUMO Toolbox and combine it with our new multi-objective model builder?

Well, you get (ideally) a pareto front which shows you which model type is most suitable for which output (or model selection metric). In addition it also shows you the correlation between different output (or metrics). This seems smarter than simply doing multiple runs in parallel.

The obligatory screenshot:
Admittedly its nothing to look at, and doesn't really give you any information, but its the proof of principle that counts :)

The context of this work is figuring out how to solve, what I like to call the 5 percent problem. I.e., an engineer wants a model to be accurate within 5%. But what does this mean, especially if you don't know what the response looks like? There are 101 ways to get 5% and it is very difficult to agree on what to use upfront while understanding the implications.

--Dirk