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        <h1 class="header">Multi-Label Classification in Python</h1>
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        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/0.1.0.tar.gz">Release: 0.1.0</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>
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      <a href="tutorial.html" class="waves-effect waves-light btn-large">Get started</a>
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       <a class="github-button" href="https://github.com/scikit-multilearn/scikit-multilearn" data-icon="octicon-star" data-show-count="true" aria-label="Star scikit-multilearn/scikit-multilearn on GitHub">Star</a>
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          <a class="github-button" href="https://github.com/scikit-multilearn/scikit-multilearn/fork" data-icon="octicon-repo-forked" data-show-count="true" aria-label="Fork scikit-multilearn/scikit-multilearn on GitHub">Fork</a>
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              <span class="card-title"><i class="fas fa-brain"></i> Lots of classifiers</span>
              <p>Scikit-multilearn provides many native Python multi-label classifiers classifiers.</p>
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              <a href="modelselection.html" class="waves-effect waves-light btn">Classifier selection</a>
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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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              <a href="labelrelations.html" class="waves-effect waves-light btn">Learn more</a>
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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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              <a href="license.html" class="waves-effect waves-light btn right-align">License</a>
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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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              <a href="datasets.html" class="waves-effect waves-light btn right-align">Learn more</a>
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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>
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              <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>
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              <a href="https://travis-ci.org/scikit-multilearn/scikit-multilearn"
               class="waves-effect waves-light"><img src="https://travis-ci.org/scikit-multilearn/scikit-multilearn.svg?branch=master" />
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               <a href="https://ci.appveyor.com/project/niedakh/scikit-multilearn/branch/master"
                class="waves-effect waves-light"><img src="https://ci.appveyor.com/api/projects/status/vd4k18u1lp5btaql/branch/master?svg=true" />
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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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              <a class="github-button" href="https://github.com/scikit-multilearn/scikit-multilearn" data-icon="octicon-star" data-show-count="true" aria-label="Star scikit-multilearn/scikit-multilearn on GitHub">Star</a>
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              <a class="github-button" href="https://github.com/scikit-multilearn/scikit-multilearn/fork" data-icon="octicon-repo-forked" data-show-count="true" aria-label="Fork scikit-multilearn/scikit-multilearn on GitHub">Fork</a>
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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>
          <!-- a href="slack.html" class="s12 l4 btn-large waves-effect waves-light"><i class="fab fa-slack"></i> Join the slack</a-->
          <a href="authors.html" class="s12 l4 btn-large waves-effect waves-light">About the project</a>
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            <h3>News</h3>
            <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>
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            <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>
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    introduction
    userguide
    authors
    benchmark
    developer
    license
