Pablo Rodríguez Belenguer

uooh Thank you, although It would be some of code for understand better, because in python I haven't found any explanaition of this..but thank you for the information.

Uoooo thank you! @ankurankan it would be great!

feature_importance = pd.DataFrame(list(zip(feature_names, np.abs(shap_values)[0].mean(0))), columns=['feature_name', 'feature_importance_vals']) feature_importance = feature_importance.iloc[(-np.abs(feature_importance['feature_importance_vals'].values)).argsort()] Before, you are putting np.abs(shap_values[0]).mean(0)), and this is no correct because you would be selecting the first row and doing the...

Thank you for your response, I am trying to map around 14.000 IDs and I have stopped the cell because for the first 800 ID I am obtaining the same...

Ok if I replace key_search by inchi, I can see this:  I think that it is correct, but in cpd_search in my opinion the information can be extracted from...

Yes! I agree, It must have some benefit to first trying the identifier. How can I extract the information only using the inchikey? Could you put the code with this...

Of course! https://github.com/topepo/caret/blob/master/models/files/tan.R This model can be train with categories which can work like strings (you use this model for do searchs of conditional probabilities). You can get whatever dataset...

Hi again, Use whatever dataset with categorical data (with strings, for example 1 variable, kind of animals: dogs, birds,cats..., another variable, size: High, medium, little etc and output variable, for...

Have you could find out something about this???

Hi! I've create a section in my github for this with a dataset totally artificial but it should be useful for using fastshap: https://github.com/PARODBE/bnlearn_r_playing/tree/main All best, Pablo