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Hi, I've just checked out Shapash. I've seen a lot of this line in the document: preprocessing=encoder, # Optional: compile step can use inverse_transform method
However, I'm not sure how to process with this. I checked the code in here, but I'm not clear of about the use of parsing dict or list_of_dict to preprocessing.
I have this example, could you please advise me how to process with it?
I recommend you to take a look at the encoding tutorials for a better understanding tutorial.
But at the moment we don't support multi label binarizer from sklearn.
We support :
from sklearn : OneHotEncoder / OrdinalEncoder / StandardScaler / QuantileTransformer / PowerTransformer
from category_encoder : OneHotEncoder / OrdinalEncoder / BaseNEncoder / BinaryEncoder / TargetEncoder
or a dict with the mapping needed
I don't know how complex your problem is but maybe you can use the features_groups of the compile step to get the importance of A,B,C or E.
Hi, I've just checked out Shapash. I've seen a lot of this line in the document:
preprocessing=encoder, # Optional: compile step can use inverse_transform method
However, I'm not sure how to process with this. I checked the code in here, but I'm not clear of about the use of parsing
dict
orlist_of_dict
topreprocessing
.I have this example, could you please advise me how to process with it?
Original df:
I want to one hot encode A and E, and multi label binarizer (B1, B2) and (C1, C2) (both encoders are from sklearn)
Target df:
Because I have multiple encoders of multiple columns, how should I pass them
preprocessing
?Thank you very much
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