Ahh Sorry for the noise Source Code never lies mlf evaluatio

Question

Ahh Sorry for the noise Source Code never lies mlf evaluation models it is Nevermind Thx anyways for that great toolkit

José Morales · Answer

Sorry for the confusion we re working on a tutorial to access the trained models parameters that will mention this

José Morales · Answer

<https nixtla github io mlforecast docs how to guides analyzing models html|This> is the guide please let us know if you feel something s missing

Philipp Leser · Answer

Hi thx for that I think that s helpfull

Philipp Leser · Answer

It s really powerfull that NIXTLA just wraps the original modles If you think that s desireable I could add another paragprag after the SHAP section where I use a Catboost model and plot the feature importance Just to bring home the point => you can access all the models and do the stuff you do with Pytorch scikit learn catboost lightGBM XGBoost for model diagnostics

José Morales · Answer

Sure go ahead We use so you ll have to modify that notebook nbs docs how to guides analyzing models ipynb We have on how to contribute but feel free to reach out if you need any help