New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Not working for multi-valued categorical features #2
Comments
I have added initial support for multi-valued categorical features (mapping to a one-hot encoder and back in DomainMapperTabular). Typically this is already done as it is required by the predictor, so could you please indicate which package you are using to directly get predictions for categorical features? |
Thanks for the reply! I worked with your updated library - now I get outputs like this:
So I remove 'fnlwgt' and 'education-num' features from adult income data and label encode the data and feed to your library.
'X' looks like this:
Then, after training I follow your code:
Can you try your code on the adult income dataset or any other dataset with multi-valued categorical features? Thanks in advance! |
I added your case as example number 2 to the example notebook. |
Does the current implementation support only binary-valued categorical features?
Because I tried with the adult income dataset which has many multi-value categorical and continuous features (https://archive.ics.uci.edu/ml/datasets/adult)
and got output like these:
Here, education and occupation are not binary features - they have many levels.
The text was updated successfully, but these errors were encountered: