I am trying to improve my KNN regression process (I would like to use sklearn / python, but it doesn't matter).I would like to improve my results and to gain insight. Here is an example:
I have data measured from an electric motor: an input voltage (U) and current (I) and an output torque (T) and speed (S).
First intend is a simple approach where I'm giving those data in the state to a KNN algorithm and I use the results. But every result is not logical (even if they are statistically close).
if you add a knowledge layer during the process, a "human in the loop approach", it must be better! For example here, you know that the input power is Pin = UI and the output power is Pout = TS. The efficiency of the system eff = Pout / Pin and the efficiency cannot be higher than 1. Whereas KNN results can generate results with eff > 1.
My question is, how to use this knowledge (additional condition/human in the loop approach) during my KNN learning process phase to improve my results? Do I continue to learn on the initial data or transformed data? Do I modify the learning process? Is it possible to add "supervisor condition" that are influencing the learning process?
Thank you for your help!