When pre processing data for machine learning. Is there any difference in using one hot encoding to turn categoric variables into numeric variables or to segment the data and the model being used along the category. So say you run a multivariate regression model on data covering 5 cities. Would a single model with one variable for each city be more better or worse than having 5 models specific for each city? Or is there no difference? Or does it depend on certain factors and intuition?
