I am facing some trouble working on a problem. I am trying to optimize a function with 34 data points (no possibility to acquire new data points in the near future) whose input is 39 dimensional and output is a single dimension scalar.
I was thinking of applying Bayesian Optimization for this but since I cannot evaluate the function at new points, is it still feasible?
What are some other models I can apply for this problem?
What if I just want to see the if there is a correlation between the input and output? (Can't use pearson correlation because it's just between a pair of variables, here I have 39+1 variables)
Hope I can get help here. Thanks in advance!