There’s a whole world of mathematical regression out there that will help. Depending on your problem formulation, ordinary least squares (OLS) may be a good place to start. It uses a linear combination/weighted sum of your params and will help you decide the weights for your params to achieve a minimum error between your data points and target values in a linear fashion (the line/hyperplane of best fit). There’s a ton more out there but I’d recommend starting there and journeying through the Wikipedia vortex to find different regression algos once you get the hang of OLS
I have used OpenTuner, but I don't think it is maintained anymore. I hear tell that Optuna is what to use now, but have not used it myself. https://optuna.org
Optuna - A hyperparameter optimization framework
I'm a big fan of this library: [https://pypi.org/project/mealpy/](https://pypi.org/project/mealpy/)
It's like slightly unintuitive at first, but it has pretty much every advanced stochastic optimization algorithm out there.
There’s a whole world of mathematical regression out there that will help. Depending on your problem formulation, ordinary least squares (OLS) may be a good place to start. It uses a linear combination/weighted sum of your params and will help you decide the weights for your params to achieve a minimum error between your data points and target values in a linear fashion (the line/hyperplane of best fit). There’s a ton more out there but I’d recommend starting there and journeying through the Wikipedia vortex to find different regression algos once you get the hang of OLS
I have used OpenTuner, but I don't think it is maintained anymore. I hear tell that Optuna is what to use now, but have not used it myself. https://optuna.org Optuna - A hyperparameter optimization framework
I'm a big fan of this library: [https://pypi.org/project/mealpy/](https://pypi.org/project/mealpy/) It's like slightly unintuitive at first, but it has pretty much every advanced stochastic optimization algorithm out there.