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Documentation of label-binarizer and multi-label format #4639
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Working on this one now. |
Releasing this one for someone else. Not working on it now. |
Is there till a need for someone to take this on? If so, it seems like a great way to get started with scikit-learn - any pointers? |
@pieteradejong feel free to get started on this. Start by reading the multi-class and multi-label documentation and make sure to understand the differences between multi-class and multi-label. |
@pieteradejong, are you still working on this? |
@andylamb yes, am working on it. have run some searches for 'multi-class' and 'multi-label' in the source, would be happy to hear about any specific locations I should look at. |
currently proceeding by looking at this file: @amueller is this the right place to look? |
@amueller there is a broken image in the URL mentioned in my previous comment; I'd be happy to reproduce it and add it back in via PR; could you provide any instructions as to how that image was produced? |
multiclass.rst is the right place to look. |
You mean the bottom right here: |
@amueller Yes, I think that's the one. Glad to see there's an existing issue. |
working on this now |
@amueller does MultiLabelBinarizer also convert the other way, that is, from a binary matrix to a list of tuples? for example, from: to: |
@amueller are you looking for any comments to be added specifically to 1.12.1, 1.12.2 or both? It seems pretty clear in the former, that you're doing multi label classification since that is the title of the section? On the other hand, the multilabel section in OneVsRest could use some more comments perhaps? |
@pieteradejong are you still working on this? If you've already raised a pr can you please pin this issue? Thanks! |
yes I am working on this |
@pieteradejong alright awesome! Waiting for a pr :) |
From the docs I feel it is not entirely clear that using label indicators means doing multi-label classification.
I think this should be better documented in the multi-class narratives, the OneVsRestClassifier and the LabelBinarizer.
Applying a label-binarizer to your y will result in quite different results, which is obvious if you know that this will switch the problem to a multi-label problem, but is somewhat unintuitive for the uninitiated.
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