How do we define objective functions while doing optimization using Pulp in Python. We have defined Prediction models separately. Next step is to bring objective functions from prediction models (Gradient boosting, Random forest , Linear regression) and optimize to achieve maximum and minimum. Please suggest if someone has worked with Pulp library.
Have a look at https://towardsdatascience.com/linear-programming-and-discrete-optimization-with-python-using-pulp-449f3c5f6e99
https://benalexkeen.com/linear-programming-with-python-and-pulp-part-2/
This short introduction to Objective functions with Pulp might help you
https://www.youtube.com/watch?v=SEliDY4wyB0
Although reading your statement it appears t me that you want to just call on optimization sets of code on certain points. you might not need Pulp for that. Python's default way of defining a function should just work fine.
In pulp you specify the objective function yourself for each problem. This isn't difficult, and the answers above show you how to do it.
If you want to modify the objective function of (say) gradient boosting I suggest using the lightgbm python library. This lets you customise the optimisation, as shown here:
https://towardsdatascience.com/custom-loss-functions-for-gradient-boosting-f79c1b40466d
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