The difference between the converged networks (or models where values for free parameters have been determined) may be more for certain datasets (DS4); and less for others (DS1).
what i mean is,
for DS1 - after repeating the process a reasonable no. of times, the network tends to reach some kind of a minima region where networks are very similar - this region seems to have close approximations of the actual relationship.
on other hand for DS4 - say, two networks converge and give very similar regression coefficient values (and sometimes even similar mse and mape values) but there is a huge difference between these networks. what is interesting is that they give very similar sensitivity analysis results.
Updates:
for example of what i am saying
- see ds4fss2days3hrsHL7_no2 and ds4fss2days3hrsHL7_no3
what, i guess, i am questioning is how well do 3 values describe the quality of the model. do they do it well - because that would mean that two models that visually look very different from each other will be the same quality. and how is it affected by consistent results from SA or the lack of consistency in results from SA.
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for example of what i am saying - see ds4fss2days3hrsHL7_no2 and ds4fss2days3hrsHL7_no3
what, i guess, i am questioning is how well do 3 values describe the quality of the model.
do they do it well - because that would mean that two models that visually look very different from each other will be the same quality.
and how is it affected by consistent results from SA or the lack of consistency in results from SA.
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