I have a
blackbox function which takes finite number of integers V1, V2, Vn parameters and based on time series variable produce a scalar response. I would like to find parameters which maximize the response. I'm not seeking the single maximum response but more a group of parameter similarities (like their ratio V1/V2, etc.) which results into well maximized response. I was reading about various techniques but cannot find the right one. Not sure if I should calculate the ratio (V1/V2, etc.) and include them in clustering together with V1...Vn? or I can apply some machine learning which tests those combinations for me.
The following code is just an illustration of the data structure, the actual number of rows is from 10K to 10M.
df = data.frame(V1=c(11L,15L,15L,16L), V2=c(20L,20L,25L,14L), V3=c(20L,15L,24L,50L), V4=c(18L,22L,30L,60L), reponse=c(1.04,1.21,0.97,1.00)) df # V1 V2 V3 V4 reponse #1 11 20 20 18 1.04 #2 15 20 15 22 1.21 #3 15 25 24 30 0.97 #4 16 14 50 60 1.00
Expected output could be list clusters of parameters, or their relationships, that produce well maximized response.