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When you can model a system using physical or known principles the data points are mostly there to prove the model is valid. Also they usually aren't randomly c
by crabbygrabby 4y ago
When you can model a system using physical or known principles the data points are mostly there to prove the model is valid. Also they usually aren't randomly chosen data points like in ML they are chosen via statistical theory.
The disadvantage of physical systems is the data is usually expensive. In my old field I know someone who had to make a decision on a data set of 5 samples and each sample costed more than a million USD.
I've made models of physical systems that involve kind of life or death stuff with less than 30 samples.
It's not for the faint of heart and if someone doesn't know what they are doing, the system they are modelling, or how to share their results correctly... Yikes!
- dekhn 4y agocan you share more about models of life and death stuff with less than 30 samples? (I work tangential to a regulated health industry)
- crabbygrabby 4y agoI'd like to stay anonymous on the matter, but I've worked in various regulated industries. Now days I do none of this .
- dekhn 4y agoUh, OK. because if you were doing life and death stuff with 30 samples, people probably died due to false negatives or positives in your model, in a way that would have been better handled with more samples. Sorry to hear it- it's one of the reasons health care is so shitty.
- crabbygrabby 4y agoNot healthcare and no one died. My models have been deployed for two years without incident.
- dekhn 4y agooh, sorry- you mean "life and death" as in you had a murderbot (https://www.youtube.com/watch?v=xuFbxeDjRZg_ https://www.youtube.com/watch?v=xuFbxeDjRZg_ was my team's contribution) that could kill people, and your model prevented it? Are you sure you can conclude your system is safe?
- crabbygrabby 4y agoNope no murder bots...