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<title>scikit-multilearn: Multi-Label Classification in Python — Multi-Label Classification for Python</title>
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<h1 class="header">Multi-Label Classification in Python</h1>
<h5 class="light sans">
Scikit-multilearn is a BSD-licensed library for multi-label classification that
is built on top of the well-known <a href="http://scikit-learn.org">scikit-learn</a> ecosystem.
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<pre class="s10"><code class="blue-grey lighten-4 pip">pip install scikit-multilearn</code></pre>
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<a class="current-version-number" href="https://github.com/scikit-multilearn/scikit-multilearn/archive/0.2.0.tar.gz">Release: 0.2.0</a>
| Supported Python versions: 2.7 / 3.x
| <a href="https://github.com/scikit-multilearn/scikit-multilearn">Github</a>
| <a href="https://pypi.org/project/scikit-multilearn">PyPi</a>
| <a href="http://scikit.ml/api/">Documentation</a>
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<a class="current-version-number" href="https://github.com/scikit-multilearn/scikit-multilearn/archive/0.1.0.tar.gz">Stable Release: 0.1.0</a>
| Supported Python versions: 2.7 / 3.x
| <a href="http://scikit.ml/api/0.1.0/userguide.html">Documentation</a>
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<span class="card-title"><i class="fas fa-clipboard-list"></i> Lots of classifiers</span>
<p>Scikit-multilearn provides many native Python multi-label classifiers classifiers.</p>
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<span class="card-title"><i class="fab fa-connectdevelop"></i> Label Relations</span>
<p>Use expert knowledge or infer label relationships from your data to improve your model.</p>
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<span class="card-title"><i class="fas fa-braille"></i> Multi-label Embeddings</span>
<p>Embedd the label space to improve discriminative ability of your classifier.</p>
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<span class="card-title"><i class="fas fa-brain"></i> Multi-label Deep Learning</span>
<p>Extend your Keras or pytorch neural networks to solve multi-label classification problems.</p>
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<span class="card-title"><i class="fas fa-bolt"></i> Efficient classification</span>
<p>Scikit-multilearn is faster and takes much less memory than the standard
stack of MULAN, MEKA & WEKA.</p>
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<a href="benchmark.html" class="waves-effect waves-light btn">Facts & Figures</a>
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<span class="card-title"><i class="fab fa-freebsd"></i> Free as in BSD</span>
<p>The licensing model follows scikit's BSD licence, to allow maximum interopability.
Some libraries if used for label space division may incur GPL requirements.</p>
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<span class="card-title"><i class="fas fa-database"></i> Data management</span>
<p>Scikit-multilearn is faster and takes much less memory than the standard
stack of MULAN, MEKA & WEKA.</p>
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<span class="card-title"><i class="fas fa-random"></i> Multi-label stratification</span>
<p>Use expert knowledge or infer label relationships from your data to improve your model.</p>
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<a href="stratification.html" class="waves-effect waves-light btn">Learn more</a>
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<span class="card-title"><i class="fas fa-box-open"></i> MEKA wrapper</span>
<p>Missing a particular classifier which exists in the Java MEKA and WEKA stack?
Now you can use it like a native scikit classifier!</p>
</div>
<div class="card-action right-align">
<a href="meka.html" class="waves-effect waves-light btn">Using MEKA</a>
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<span class="card-title"><i class="fas fa-wrench"></i> Well maintained</span>
<p>Scikit-multilearn has over 82% test coverage and undergoes continous integration on Windows 10, OS X and Ubuntu.</p>
</div>
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<span class="card-title"><i class="fab fa-python"></i> Scikit-compatible</span>
<p>Scikit-multilearn is compatible with the Scipy and scikit-learn stack. Use our classifiers with scikit,
use scikit classifiers with our code.</p>
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<span class="card-title"><i class="fab fa-github"></i> Widely used</span>
<p>With over 160 stars and 60 forks scikit-multilearn is the second most popular multi-label library on github.</p>
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<span class="card-title"><i class="fab fa-stack-overflow"></i> We're on StackOverflow</span>
<p>Need help? Ask a question on Stack Overflow, our community will answer.</p>
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<a href="https://stackoverflow.com/tags/scikit-multilearn" class="waves-effect waves-light btn">#scikit-multilearn on SO</a>
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<h3 class="light header">Learn more</h3>
<p class="col s12 m8 offset-m2 caption">Scikit-multilearn offers extensive user documentation. Read the user docs, learn from recipes constructed on real data or browse the API reference to find a concrete class or function.</p>
<a href="userguide.html" class="s12 l4 btn-large waves-effect waves-light">User docs</a>
<a href="api/skmultilearn.html" class="s12 l4 btn-large waves-effect waves-light">Reference</a>
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<h3 class="light header">Join the team!</h3>
<p class="col s12 m8 offset-m2 caption">Scikit-multilearn is developed</p>
<a href="developer.html" class="s12 l4 btn-large waves-effect waves-light"><i class="fab fa-python"></i> Developer docs</a>
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<h3>News</h3>
<h5>0.2.0 (released 2018-12-10)</h5>
<p>A new feature release:</p>
<ul class="browser-default">
<li>first python implementation of multi-label SVM (MLTSVM)</li>
<li>a general multi-label embedding framework with several embedders supported (LNEMLC, CLEMS)</li>
<li>balanced k-means clusterer from HOMER implemented</li>
<li>wrapper for Keras model use in scikit-multilearn</li>
</ul>
<hr />
<h5>0.1.0 [stable] (released 2018-09-04)</h5>
<p>Fix a lot of bugs and generally improve stability, cross-platform functionality standard
and unit test coverage. This release has been tested with a large set of unit tests that
work across Windows.
Also, new features:</p>
<ul class="browser-default">
<li>multi-label stratification algorithm and stratification quality measures</li>
<li>a robust reorganization of label space division, alongside with a working stochastic blockmodel approach and new
underlying layer - graph builders that allow using graph models for dividing the label space based not just on
label co-occurence but on any kind of network relationships between labels you can come up with</li>
<li>meka wrapper works fully cross-platform now, including windows 10</li>
<li>multi-label data set downloading and load/save functionality brought in, like sklearn's dataset</li>
<li>kNN models support sparse input</li>
<li>MLARAM models support sparse input</li>
<li>BSD-compatible label space partitioning via NetworkX</li>
<li>dependence on GPL libraries made optional</li>
<li>working predict_proba added for label space partitioning methods</li>
<li>MLARAM moved to from neurofuzzy to adapt</li>
<li>test coverage increased to 94%</li>
<li>Classifier Chains allow specifying the chain order</li>
<li>lots of documentation updates</li>
</ul>
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accesskey="I">index</a></li>
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>modules</a> |</li>
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<h5 class="white-text">Cite US!</h5>
<p>If you use scikit-multilearn in your research and publish it, please consider citing us, it will help us get funding for making the library better. The paper is available on <a href="https://arxiv.org/abs/1702.01460">arXiv</a>, to cite it try the Bibtex code on the right.</p>
</div>
<div class="col l4 s12">
<pre><code>
@ARTICLE{2017arXiv170201460S,
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},
primaryClass = "cs.LG",
keywords = {Computer Science - Learning, Computer Science - Mathematical Software},
year = 2017,
month = feb,
}
</code></pre>
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