I have a number of machines of different configurations, and for each machine, I monitor a couple of parameters and build a linear model for productivity ~ monitored parameters. Now I need to predict productivity for some new machines which do not report productivity back to us. My plan is to cluster those models into groups (probably build a groupwise model) and link each group to some configuration range (some configurations are numerical). So I can assign the new machine into a group based on its configuration and apply that gorupwise model on it. What is the best way to cluster those models?
Updated: It might be easier to explain it using another example, say I have this data
studentID IQ breakfast_consumption grade 1 100 50 3 1 100 100 4 2 80 100 4 2 80 80 2
I build models for each student with grade ~ breakfast_consumption. Now we have a new student and we have his IQ and breakfast_consumption. How can I predict his grade? By the way, I did try to build a model for all students with
grade ~ IQ + breakfast_consumption, but the r2 is much lower than the individual models.