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This might be a stupid question but I was just wondering what the difference between ML-KNN implemented in scikit.ml and scikit-learn's KNeighborsClassifier is. According to sklearn's docs KNeighborsClassifier has support for multilabel classification. ML-KNN however is KNN adapted for multilabel classification built on top of sklearn's architecture based on it's docs.

When searching for sample multilabel problems, MLkNN mostly appears but I do not understand if there's any advantage of using it over the base implementation of sklearn if it already supports it. Is it only a late adaptation in sklearn's side or are there more differences in the implementation?

Any input is appreciated. Thanks!

iamnobody
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scikit-multilearn's ML-KNN implementations is an improved version of scikit-learn's KNeighborsClassifier. It is actually built on top of it. After the k nearest neighbors in the training data are found, it uses maximum a posteriori principle to label a new instance to achieve a better performance. Also, since it operates on sparse matrices internally using SciPy sparse matrix library, it is highly memory-efficient. More info here and here.

Reveille
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    Thanks for the response and for the paper links! A follow up quesion: would you know how sklearn's KNN multilabel support functions? I know that membership for multi-class/binary classification is based on maximum membership ie. the majority class that the neighbors are in. If ML-KNN does maximum a posteriori instead, how is multilabel membership handled in the KNeighborsClassifier? – iamnobody Sep 15 '19 at 16:45