Optimizing simulation input parameters to fit statistical data in Python
Regression models are fit on training data using linear regression and local search optimization algorithms.
Models like linear regression and logistic regression are trained by least squares optimization, and this is the most efficient approach to finding coefficients that minimize error for these models.
Nevertheless, it is possible to use alternate optimization algorithms to fit a regression model to a training dataset. This can be a useful exercise to learn more about how regression functions and the central nature of optimization in applied machine learning. It may also be required for regression with data that does not meet the requirements of a least squares optimization procedure.
I want to optimize four input parameters on a numerical model. I have an input file where I have these parameters. I run an application in Python using subprocess and obtain the results on csv files. I run these simulations around 300 times to have some Monte Carlo simulations, obtaining a range of possible values to compare with real data (20 points that follow a Weibull distribution) I have.
Which optimization algorithm can I use with the goodness of fit from the quartiles between numerical results and real data (this is the OF) to get optimal initial parameters?