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imbalanced-learn documentation
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**Date**: |today| **Version**: |version|

**Useful links**:
`Binary Installers <https://pypi.org/project/imbalanced-learn>`__ |
`Source Repository <https://github.com/scikit-learn-contrib/imbalanced-learn>`__ |
`Issues & Ideas <https://github.com/scikit-learn-contrib/imbalanced-learn/issues>`__ |
`Q&A Support <https://gitter.im/scikit-learn-contrib/imbalanced-learn>`__

Imbalanced-learn (imported as :mod:`imblearn`) is an open source, MIT-licensed
library relying on scikit-learn (imported as :mod:`sklearn`) and provides tools
when dealing with classification with imbalanced classes.

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                    <i class="fas fa-download fa-7x"></i>
                    <h5 class="card-title">Getting started</h5>
                    <p class="card-text">Check out the getting started guides to install <em>imbalanced-learn</em>.
                    Some extra information to get started with a new contribution is also provided.</p>

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    :ref:`To the installation guideline<getting_started>`

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                    <h5 class="card-title">User guide</h5>
                    <p class="card-text">The user guide provides in-depth information on the
                    key concepts of imbalanced-learn with useful background information and explanation.</p>

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    :ref:`To the user guide<user_guide>`

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                    <h5 class="card-title">API reference</h5>
                    <p class="card-text">The reference guide contains a detailed description of
                    the imbalanced-learn API. To known more about methods parameters.</p>

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    :ref:`To the reference guide<api>`

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                    <h5 class="card-title">Examples</h5>
                    <p class="card-text">The gallery of examples is a good place to see imbalanced-learn in action.
                    Select an example and dive in.</p>

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    :ref:`To the gallery of examples<general_examples>`

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.. toctree::
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    :titlesonly:

    install
    user_guide
    references/index
    auto_examples/index
    whats_new
    about