I am trying to find out the best way to fit different probabilistic models (like Latent Dirichlet Allocation, Non-negative Matrix Factorization, etc) on sklearn (Python).
Looking at the example in the sklearn documentation, I was wondering why the LDA model is fit on a TF array, while the NMF model is fit on a TF-IDF array. Is there a precise reason for this choice?
Also, any tips about how to find the best parameters (number of iterations, number of topics...) for fitting my models is well accepted.
Thank you in advance.