Hey! I have another question: I am using MLForecast with a sklearn pipeline including some one hot encoding step. I want to do some feature importance plots and I'm having issues getting a dataframe with all the final feature columns (dynamic features from MLForecast + onehot encoded columns). Any idea / hint to get such a dataframe? Any idea is much appreciated! I would go ahead and hack something together with the before_predict_callback printing out the column names. Greetings from Germany and thank you very much in advance 🙂

hey @Florian Stracke! Thanks for using mlforecast. One option might be to use

OneHotEncoder

directly on the data before using

MLForecast

and feed the

preprocess

method with that preprocessed data. That will give you the dynamic features and the one hot encoded columns. Something like: