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scikit-multilearn is a Python module capable of performing multi-label learning tasks. It is built on-top of various scientific Python packages (numpy, scipy) and follows a similar API to that of scikit-learn.


  • Native Python implementation. A native Python implementation for a variety of multi-label classification algorithms. To see the list of all supported classifiers, check this link.

  • Interface to Meka. A Meka wrapper class is implemented for reference purposes and integration. This provides access to all methods available in MEKA, MULAN, and WEKA — the reference standard in the field.

  • Builds upon giants! Team-up with the power of numpy and scikit. You can use scikit-learn’s base classifiers as scikit-multilearn’s classifiers. In addition, the two packages follow a similar API.


In most cases you will want to follow the requirements defined in the requirements/*.txt files in the package.

Base dependencies

liac-arff # for loading ARFF files
requests # for dataset module
networkx # for networkX base community detection clusterers
python-louvain # for networkX base community detection clusterers

GPL-incurring dependencies for two clusterers

python-igraph # for igraph library based clusterers
python-graphtool # for graphtool base clusterers

Note: Installing graphtool is complicated, please see: graphtool install instructions


To install scikit-multilearn, simply type the following command:

$ pip install scikit-multilearn

This will install the latest release from the Python package index. If you wish to install the bleeding-edge version, then clone this repository and run

$ git clone
$ cd scikit-multilearn
$ python

Basic Usage

Before proceeding to classification, this library assumes that you have a dataset with the following matrices:

  • x_train, x_test: training and test feature matrices of size (n_samples, n_features)
  • y_train, y_test: training and test label matrices of size (n_samples, n_labels)

Suppose we wanted to use a problem-transformation method called Binary Relevance, which treats each label as a separate single-label classification problem, to a Support-vector machine (SVM) classifier, we simply perform the following tasks:

# Import BinaryRelevance from skmultilearn
from skmultilearn.problem_transform import BinaryRelevance

# Import SVC classifier from sklearn
from sklearn.svm import SVC

# Setup the classifier
classifier = BinaryRelevance(classifier=SVC(), require_dense=[False,True])

# Train, y_train)

# Predict
y_pred = classifier.predict(X_test)

More examples and use-cases can be seen in the documentation. For using the MEKA wrapper, check this link.


This project is open for contributions. Here are some of the ways for you to contribute:

  • Bug reports/fix
  • Features requests
  • Use-case demonstrations
  • Documentation updates

In case you want to implement your own multi-label classifier, please read our Developer’s Guide to help you integrate your implementation in our API.

To make a contribution, just fork this repository, push the changes in your fork, open up an issue, and make a Pull Request!

We’re also available in Slack! Just go to our slack group.


If you used scikit-multilearn in your research or project, please cite our work:

   author = {{Szyma{\'n}ski}, P. and {Kajdanowicz}, T.},
   title = "{A scikit-based Python environment for performing multi-label classification}",
   journal = {ArXiv e-prints},
   archivePrefix = "arXiv",
   eprint = {1702.01460},
   year = 2017,
   month = feb