- I want to obtain maximum performance out of a process with many variables, many of which cannot be controlled.
- I cannot run thousands of experiments, so it’d be nice if I could run hundreds of experiments and
- vary many controllable parameters
- collect data on many parameters indicating performance
- ‘correct,’ as much as possible, for those parameters I couldn’t control
- Tease out the ‘best’ values for those things I can control, and start all over again
It feels like this would be called data mining, where you’re going through tons of data which doesn’t immediately appear to relate, but does show correlation after some effort.
So… Where do I start looking at algorithms, concepts, theory of this sort of thing? Even related terms for purposes of search would be useful.
Background: I like to do ultra-marathon cycling, and keep logs of each ride. I’d like to keep more data, and after hundreds of rides be able to pull out information about how I perform.
However, everything varies – routes, environment (temp, pres., hum., sun load, wind, precip., etc), fuel, attitude, weight, water load, etc, etc, etc. I can control a few things, but running the same route 20 times to test out a new fuel regime would just be depressing, and take years to perform all the experiments that I’d like to do. I can, however, record all these things and more(telemetry on bicycle FTW).
It sounds like you want to do some regression analysis. You certainly have plenty of data!
Regression analysis is an extremely common modeling technique in statistics and science. (It could be argued that statistics is the art and science of regression analysis.) There are many statistics packages out there to do the computation you’ll need. (I’d recommend one, but I’m years out of date.)
Data mining has gotten a bad name because far too often people assume correlation equals causation. I found that a good technique is to start with variables you know have an influence and build a statistical model around them first. So you know that wind, weight and climb have an influence on how fast you can travel and statistical software can take your dataset and calculate what the correlation between those factors are. That will give you a statistical model or linear equation:
When you explore new variables, you will be able to see if the model is improved or not by comparing a goodness of fit metric like R-squared. So you might check if temperature or time of day adds anything to the model.
You may want to apply a transformation to you data. For instance, you might find that you perform better on colder days. But really cold days and really hot days might hurt performance. In that case, you could assign temperatures to bins or segments: < 0°C; 0°C to 40°C; > 40°C, or some such. The key is to transform the data in a way that matches a rational model of what is going on in the real world, not just the data itself.
In case someone thinks this is not a programming related topic, notice that you can use these same techniques to analyze system performance.