I have produced a regression model in R and saved it as a .pmml. Related to this question, I am trying to run a code that uses predicted values of the model to compute some objects of interest. It looks like this:
from pypmml import Model
model=model = Model.fromFile('C://Users///my_model.pmml')
model.inputNames
['X1', 'Tax', 'X2', 'X3', 'X4', 'X5']
For some computations, I would like to change the coefficient associated with 'Tax' while letting the rest of the model stay the same. It is clear that I could also do so in R and then store the model, but so far this producing some errors, so I wonder if Python would be more friendly for this.
Do you have any suggestions?