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<!DOCTYPE html>
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<title>scikit-multilearn: Multi-Label Classification in Python — Multi-Label Classification for Python</title>
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<li><a href="#">User Guide</a></li>
<li><a href="api/skmultilearn.html">Reference</a></li>
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<li><a class="reference internal" href="#">User Guide</a><ul>
<li><a class="reference internal" href="#introduction">1. Introduction</a></li>
<li><a class="reference internal" href="#advanced-usage">2. Advanced usage</a></li>
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<div class="section" id="user-guide">
<h1>User Guide<a class="headerlink" href="#user-guide" title="Permalink to this headline">¶</a></h1>
<div class="section" id="introduction">
<h2>1. Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">¶</a></h2>
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<ul>
<li class="toctree-l1"><a class="reference internal" href="tutorial.html">1. Getting started with scikit-multilearn</a></li>
<li class="toctree-l1"><a class="reference internal" href="concepts.html">2. Relevant Concepts in Multi-Label Classification</a><ul>
<li class="toctree-l2"><a class="reference internal" href="concepts.html#Aim">2.1. Aim</a></li>
<li class="toctree-l2"><a class="reference internal" href="concepts.html#Single-label-vs-multi-label-classification">2.2. Single-label vs multi-label classification</a></li>
<li class="toctree-l2"><a class="reference internal" href="concepts.html#Multi-label-classification-data">2.3. Multi-label classification data</a><ul>
<li class="toctree-l3"><a class="reference internal" href="concepts.html#The-multi-label-data-representation">2.3.1. The multi-label data representation</a></li>
<li class="toctree-l3"><a class="reference internal" href="concepts.html#Single-label-representations-in-problem-transformation">2.3.2. Single-label representations in problem transformation</a></li>
</ul>
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</ul>
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<li class="toctree-l1"><a class="reference internal" href="datasets.html">3. Dataset handling</a><ul>
<li class="toctree-l2"><a class="reference internal" href="datasets.html#scikit-multilearn-format">3.1. scikit-multilearn format</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="datasets.html#scikit-multilearn-repository">4. scikit-multilearn repository</a><ul>
<li class="toctree-l2"><a class="reference internal" href="datasets.html#ARFF-files">4.1. ARFF files</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="modelselection.html">5. How to select a classifier</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modelselection.html#Intutions">5.1. Intutions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modelselection.html#Generalization-quality-measures">5.1.1. Generalization quality measures</a></li>
<li class="toctree-l3"><a class="reference internal" href="modelselection.html#Performance">5.1.2. Performance</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="modelselection.html#Data-driven-model-selection">5.2. Data-driven model selection</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modelselection.html#Estimating-hyper-parameter-k-for-MLkNN">5.2.1. Estimating hyper-parameter k for MLkNN</a></li>
<li class="toctree-l3"><a class="reference internal" href="modelselection.html#Estimating-hyper-parameter-k-for-embedded-classifiers">5.2.2. Estimating hyper-parameter k for embedded classifiers</a></li>
</ul>
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<div class="section" id="advanced-usage">
<h2>2. Advanced usage<a class="headerlink" href="#advanced-usage" title="Permalink to this headline">¶</a></h2>
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<li class="toctree-l1"><a class="reference internal" href="labelrelations.html">1. Exploring Label Relations</a><ul>
<li class="toctree-l2"><a class="reference internal" href="labelrelations.html#Detecting-communities-in-Label-Relations-Graph">1.1. Detecting communities in Label Relations Graph</a><ul>
<li class="toctree-l3"><a class="reference internal" href="labelrelations.html#Building-a-Label-Graph">1.1.1. Building a Label Graph</a></li>
<li class="toctree-l3"><a class="reference internal" href="labelrelations.html#NetworkX">1.1.2. NetworkX</a></li>
</ul>
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</ul>
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<li class="toctree-l1"><a class="reference internal" href="labelrelations.html#Using-iGraph">2. Using iGraph</a></li>
<li class="toctree-l1"><a class="reference internal" href="labelrelations.html#Stochastic-Blockmodel-from-graph-tool">3. Stochastic Blockmodel from graph-tool</a><ul>
<li class="toctree-l2"><a class="reference internal" href="labelrelations.html#Using-scikit-learn-clusterers">3.1. Using scikit-learn clusterers</a></li>
<li class="toctree-l2"><a class="reference internal" href="labelrelations.html#Fixed-partition-based-on-expert-knowledge">3.2. Fixed partition based on expert knowledge</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="meka.html">4. Using the MEKA wrapper</a><ul>
<li class="toctree-l2"><a class="reference internal" href="meka.html#Setting-up-MEKA">4.1. Setting up MEKA</a></li>
<li class="toctree-l2"><a class="reference internal" href="meka.html#Using-MEKA-via-scikit-multilearn">4.2. Using MEKA via scikit-multilearn</a></li>
<li class="toctree-l2"><a class="reference internal" href="meka.html#Citing-meka">4.3. Citing meka</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="multilabeldnn.html">5. Multi-label deep learning with scikit-multilearn</a><ul>
<li class="toctree-l2"><a class="reference internal" href="multilabeldnn.html#Keras">5.1. Keras</a><ul>
<li class="toctree-l3"><a class="reference internal" href="multilabeldnn.html#Single-class-Keras-classifier">5.1.1. Single-class Keras classifier</a></li>
<li class="toctree-l3"><a class="reference internal" href="multilabeldnn.html#Multi-class-Keras-classifier">5.1.2. Multi-class Keras classifier</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="multilabeldnn.html#Pytorch">5.2. Pytorch</a><ul>
<li class="toctree-l3"><a class="reference internal" href="multilabeldnn.html#Single-class-pytorch-classifier">5.2.1. Single-class pytorch classifier</a></li>
<li class="toctree-l3"><a class="reference internal" href="multilabeldnn.html#Multi-class-pytorch-classifier">5.2.2. Multi-class pytorch classifier</a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="multilabelembeddings.html">6. Multi-label embedding-based classification</a><ul>
<li class="toctree-l2"><a class="reference internal" href="multilabelembeddings.html#Label-Network-Embeddings">6.1. Label Network Embeddings</a></li>
<li class="toctree-l2"><a class="reference internal" href="multilabelembeddings.html#Cost-Sensitive-Label-Embedding-with-Multidimensional-Scaling">6.2. Cost-Sensitive Label Embedding with Multidimensional Scaling</a></li>
<li class="toctree-l2"><a class="reference internal" href="multilabelembeddings.html#Scikit-learn-based-embedders">6.3. Scikit-learn based embedders</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="stratification.html">7. Multi-label data stratification</a></li>
</ul>
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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>
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<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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