Add multi-label Classification Support #minor #76
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In the reference paper the exlusive policies make the selection of positive or negative examples based on the notion of the "most specific class" of instance, for example ["1", "1.1"] having the most specific class "1.1". This was not respected before.
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## main #76 +/- ##
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The tag skips the check_estimator_sparse_data in the sklearn compatiable_estimator suit. This is done since sklearn requires the result to be at most 2 dimensional, however the multi-label output has 3 dimensions.
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For some reason the documentation build is failing with the last commits. @agemcipe can you please take a look? Here is the log and I can also give you access to read the docs if it makes things easier. |
mirand863
requested changes
Jan 16, 2024
Co-authored-by: mirand863 <[email protected]>
Co-authored-by: mirand863 <[email protected]>
Co-authored-by: mirand863 <[email protected]>
mirand863
approved these changes
Jan 31, 2024
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The goal of this PR is to implement multi-label hierarchical classification thereby lifting the restriction that the target variable has to correspond to a single well-defined path from root to most specific class in the hierarchy. Instead, multiple paths can be specified and in turn, trained on and predicted. Closes #23
Additionally, this PR also fixes #75