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I also witnesses this first hand at a Biotech company I worked at... we were using many variants of machine learning algorithms to develop predictive models of
by cashsterling 6y ago
I also witnesses this first hand at a Biotech company I worked at... we were using many variants of machine learning algorithms to develop predictive models of cell culture and separation processes. Problem is... the models have so many parameters in order to get a useful fit that the same model can also fit a carrot or an elephant.
We found that dynamic parameter estimation on ODE/DAE/PDE system models, while harder to develop, actually worked much better and gave us real insight into the processes.
So now my advice is others is "if you can start with some first principles equation or system of equations... start there and use optimization/regression to fit the model to the data."
AND: "if you don't think such equations exist for your problem... read/research more, because some useful equations probably do exist."
This is usually pretty straightforward for engineering and science applications... equations exist or can be derived for the system under study.
In my very limited exposure to other areas of machine learning application... I have found quite a bit of mathematical science related to marketing, human behavior, etc.
- x86_64Ubuntu 6y agoKind of weird that they would use ML/AI for a separations process. Separations and chemical engineering in general absolutely LOVES parameters and systems of equations. And don't go anywhere near colloids, those have so many empirically sourced parameters it will make your head spin.
- xkcd-sucks 6y agoYou do this when you're a vendor working for big pharma and the people there can't/won't give you/don't understand the relevant quantities and aren't even familiar with standard models of the process despite their being formally trained chemical engineers and the models being 50 years old. Speaking from direct experience with several companies that are trying to bring us covid vaccines
- alephu5 6y agoI completely agree with this sentiment, I've seen a lot of people throw ML at problems because they don't know much mathematics. Especially when you have a lot of data, I can understand the allure of just wiring up the input & output to generate the model.
- andi999 6y agoDyson asked Fermi about his take on his model fitting with four parameters. The reply was: I remember my friend Johnny von Neumann used to say, with four parameters I can fit an elephant, and with five I can make him wiggle his trunk.
- pkaye 6y agoAlso reminds me this one: > Everything is linear if plotted log-log with a fat magic marker
- sadfklsjlkjwt 6y agoFeature engineering is a big part of ML. If you know something about the process you should incorporate that..
- bluishgreen 6y agoYou make it sound like "overfitting" is not an available concept machine learning researchers have even heard of or dealt with. If you tell me you don't have enough data that even the smallest model you can generate will still overfit, then that is another matter. But there are well known techniques and analytical processes to overcome overfitting. Also using situation specific equations is a common practice in ML and is usually referred to as Feature Engineering.