Description:

scikit-multilearn is a Python library for performing multi-label classification. The library is compatible with the scikit/scipy ecosystem and uses sparse matrices for all internal operations. It provides native Python implementations of popular multi-label classification methods alongside novel framework for label space partitioning and division. It includes graph-based community detection methods that utilize the powerful igraph library for extracting label dependency information. In addition its code is well test covered and follows PEP8. Source code and documentation can be downloaded from http://scikit.ml and also via pip. The library follows scikit's BSD licencing scheme.

Changes to previous version:

• a general matrix-based label space clusterer has been added which can cluster the output space using any scikit-learn compatible clusterer (incl. k-means)
• support for more single-class and multi-class classifiers you can now use problem transformation approaches with your favourite neural networks/deep learning libraries: theano, tensorflow, keras, scikit-neuralnetworks
• support for label powerset based stratified kfold added
• graph-tool clusterer supports weighted graphs again and includes stochastic blockmodel calibration
• bugs were fixed in: classifier chains and hierarchical neuro fuzzy clasifiers

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Agnostic

Data Formats: Arff, Numpy, Scipy

Tags: Machine Learning, Large Datasets, Multi Label

Archive: download here

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