Comments (3)
Ah, I mixed up multiclass and multilabel.
I do assume that this is not implemented yet, it also doesn't work when porting to other languages.
You might be able to work-around with splitting each label up into it's own binary classification RFC. However, this will only work satisfactory if the labels are assumed to be independent (which is most likely not the case).
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The JS example itself is using multilabel with 3 different labels. So I assume that this should work.
What are the labels you are using? There are known problems when using labels that are non-sequential ints, e.g. labels [0,1,3]
instead of [0,1,2]
(#37)
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First of all, thanks for your response! I am using binary labels, so as a prediction I should get a list of true/false for each label.
For example:
[0,1,1,0,0,0,0,1,0]
This prediction i mapping back to the string labels.
PS: I don't know, if you understood my problem right. I don't want to predict one label out of multiple possible label (like in the iris dataset example you provided). I want to predict multiple labels out of all of possible labels.
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Related Issues (20)
- Feature Request: translator for onehot encoder
- Feature Request: Multinomial Logistic Regression
- A bug : When the version of sklearn contains character sequences like "rc1, rc2", the Porter class cannot be created. HOT 1
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- Test code, which is part of the Readme is failing HOT 2
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- [Error] Works fine with C but getting this error when ported to Java
- OSError: Windows isn't supported yet HOT 3
- Unable to check integrity score. HOT 1
- Generating probabilities instead of categorical results
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- Is there any plan to support RandomForestRegressor? HOT 11
- ImportError: cannot import name 'Porter' HOT 2
- Can't use port or save functions HOT 3
- ModuleNotFoundError: No module named 'sklearn_porter' HOT 1
- ModuleNotFoundError: No module named 'sklearn.tree.tree' HOT 2
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