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[P] Machine Learning for error correction – What sort of model should i use?

Hallo!

I currently work on a software product that interfaces with a hardware device. The hardware device takes a set of 5 Parameters. When the software installs, we are able to generate these 5 parameters for the customers specific hardware to a “close enough” degree of accuracy using basic physics and math calculations for the device. However, i have noticed with my logging that in most cases the customers are having to adjust these parameters up to +- 5% to get to an optimal value.

If instead of just logging these parameter changes, would i be able to feed them into an ML model of some kind which would then be able to learn for which values i generate, need to be adjusted by a small amount?
My ML experience so far is mainly just predictive models using Naive Bayes, a few genetiv algorithms and LSTM models for algo trading. However i am not sure how i should approach this problem so i am interested in any insight.

Thanks!

submitted by /u/MopicBrett
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.