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‎doc/_static/css/imbalanced-learn.css‎

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@import url("theme.css");
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.highlight a {
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text-decoration: underline;
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text-decoration: underline;
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}
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.deprecated p {
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padding: 10px 7px 10px 10px;
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color: #b94a48;
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background-color: #F3E5E5;
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border: 1px solid #eed3d7;
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padding: 10px 7px 10px 10px;
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color: #b94a48;
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background-color: #f3e5e5;
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border: 1px solid #eed3d7;
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}
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.deprecated p span.versionmodified {
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font-weight: bold;
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font-weight: bold;
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}
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.wy-nav-content {
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max-width: 1200px !important;
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max-width: 1200px !important;
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}
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/* Override some aspects of the pydata-sphinx-theme */
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/* Getting started index page */
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.intro-card {
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background: #fff;
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border-radius: 0;
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padding: 30px 10px 10px 10px;
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margin: 10px 0px;
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}
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.intro-card .card-text {
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margin: 20px 0px;
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/*min-height: 150px; */
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}
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.custom-button {
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background-color: #dcdcdc;
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border: none;
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color: #484848;
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text-align: center;
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text-decoration: none;
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display: inline-block;
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font-size: 0.9rem;
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border-radius: 0.5rem;
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max-width: 220px;
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padding: 0.5rem 0rem;
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}
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.custom-button a {
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color: #484848;
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}
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.custom-button p {
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margin-top: 0;
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margin-bottom: 0rem;
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color: #484848;
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}

‎doc/conf.py‎

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# The name of the Pygments (syntax highlighting) style to use.
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pygments_style = "sphinx"
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# -- Options for math equations -----------------------------------------------
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extensions.append("sphinx.ext.imgmath")
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imgmath_image_format = "svg"
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# -- Options for HTML output ----------------------------------------------
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# The theme to use for HTML and HTML Help pages. See the documentation for
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html_favicon = "_static/img/favicon.ico"
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html_logo = "_static/img/logo.png"
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html_style = "css/imbalanced-learn.css"
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html_css_files = [
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"css/imbalanced-learn.css",
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]
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html_theme_options = {
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"github_url": "https://github.com/scikit-learn-contrib/imbalanced-learn",
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# see https://github.com/numpy/numpydoc/issues/69
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numpydoc_show_class_members = False
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# -- Options for math equations -----------------------------------------------
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extensions.append("sphinx.ext.imgmath")
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imgmath_image_format = "svg"
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# -- Options for sphinxcontrib-bibtex -----------------------------------------
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# bibtex file
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"scipy": ("https://docs.scipy.org/doc/scipy/reference", None),
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"matplotlib": ("https://matplotlib.org/", None),
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"sklearn": ("http://scikit-learn.org/stable", None),
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"pandas": ("http://pandas.pydata.org/pandas-docs/stable/", None),
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}
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# -- Options for sphinx-gallery -----------------------------------------------

‎doc/datasets/index.rst‎

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Counter({'Iris-virginica': 50, 'Iris-versicolor': 20, 'Iris-setosa': 10})
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See :ref:`sphx_glr_auto_examples_datasets_plot_make_imbalance.py` and
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:ref:`sphx_glr_auto_examples_plot_sampling_strategy_usage.py`.
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:ref:`sphx_glr_auto_examples_api_plot_sampling_strategy_usage.py`.

‎doc/index.rst‎

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You can adapt this file completely to your liking, but it should at least
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contain the root `toctree` directive.
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###############################
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imbalanced-learn documentation!
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###############################
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:notoc:
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.. toctree::
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:maxdepth: 2
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:hidden:
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:caption: Getting Started
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##############################
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imbalanced-learn documentation
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##############################
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install
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**Date**: |today| **Version**: |version|
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.. toctree::
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:maxdepth: 2
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:hidden:
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:caption: Documentation
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**Useful links**:
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`Binary Installers <https://pypi.org/project/imbalanced-learn>`__ |
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`Source Repository <https://github.com/scikit-learn-contrib/imbalanced-learn>`__ |
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`Issues & Ideas <https://github.com/scikit-learn-contrib/imbalanced-learn/issues>`__ |
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`Q&A Support <https://gitter.im/scikit-learn-contrib/imbalanced-learn>`__
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user_guide
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references/index
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Imbalanced-learn (imported as :mod:`imblearn`) is an open source, MIT-licensed
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library relying on scikit-learn (imported as :mod:`sklearn`) and provides tools
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when dealing with classification with imbalanced classes.
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.. toctree::
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:maxdepth: 2
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:hidden:
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:caption: Tutorial - Examples
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.. raw:: html
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auto_examples/index
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<div class="container">
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<div class="row">
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<div class="col-lg-6 col-md-6 col-sm-6 col-xs-12 d-flex">
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<div class="card text-center intro-card shadow">
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<div class="card-body flex-fill">
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<i class="fas fa-download fa-7x"></i>
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<h5 class="card-title">Getting started</h5>
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<p class="card-text">Check out the getting started guides to install <em>imbalanced-learn</em>.
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Some extra information to get started with a new contribution is also provided.</p>
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.. toctree::
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:maxdepth: 1
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:hidden:
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:caption: Addtional Information
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.. container:: custom-button
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whats_new
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about
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:ref:`To the installation guideline<getting_started>`
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`Getting started <install.html>`_
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---------------------------------
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.. raw:: html
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Information to install, test, and contribute to the package.
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</div>
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</div>
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</div>
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<div class="col-lg-6 col-md-6 col-sm-6 col-xs-12 d-flex">
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<div class="card text-center intro-card shadow">
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<div class="card-body flex-fill">
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<i class="fas fa-book fa-7x"></i>
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<h5 class="card-title">User guide</h5>
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<p class="card-text">The user guide provides in-depth information on the
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key concepts of imbalanced-learn with useful background information and explanation.</p>
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`User Guide <user_guide.html>`_
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-------------------------------
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.. container:: custom-button
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The main documentation. This contains an in-depth description of all
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algorithms and how to apply them.
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:ref:`To the user guide<user_guide>`
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`API Documentation <references/index.html>`_
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-------------------------------------------
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.. raw:: html
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The exact API of all functions and classes, as given in the
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doctring. The API documents expected types and allowed features for
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all functions, and all parameters available for the algorithms.
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</div>
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</div>
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</div>
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<div class="col-lg-6 col-md-6 col-sm-6 col-xs-12 d-flex">
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<div class="card text-center intro-card shadow">
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<div class="card-body flex-fill">
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<i class="fas fa-code fa-7x"></i>
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<h5 class="card-title">API reference</h5>
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<p class="card-text">The reference guide contains a detailed description of
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the imbalanced-learn API. To known more about methods parameters.</p>
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`Examples <auto_examples/index.html>`_
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--------------------------------------
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.. container:: custom-button
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A set of examples illustrating the use of the different algorithms. It
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complements the `User Guide <user_guide.html>`_.
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:ref:`To the reference guide<api>`
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`What's new <whats_new.html>`_
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------------------------------
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.. raw:: html
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Log of the imbalanced-learn history.
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</div>
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</div>
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</div>
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<div class="col-lg-6 col-md-6 col-sm-6 col-xs-12 d-flex">
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<div class="card text-center intro-card shadow">
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<div class="card-body flex-fill">
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<i class="fas fa-chart-bar fa-7x"></i>
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<h5 class="card-title">Examples</h5>
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<p class="card-text">The gallery of examples is a good place to see imbalanced-learn in action.
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Select an example and dive in.</p>
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`About imbalanced-learn <about.html>`_
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--------------------------------------
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.. container:: custom-button
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Just to know about history of imbalanced-learn.
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:ref:`To the gallery of examples<general_examples>`
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See the `README <https://github.com/scikit-learn-contrib/imbalanced-learn/blob/master/README.rst>`_
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for more information.
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.. raw:: html
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</div>
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</div>
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</div>
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</div>
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</div>
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.. toctree::
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:maxdepth: 3
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:hidden:
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:titlesonly:
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install
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user_guide
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references/index
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auto_examples/index
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whats_new
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about

‎doc/install.rst‎

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#######
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Install
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#######
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.. _getting_started:
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###############
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Getting Started
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###############
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Prerequisites
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=============

‎doc/introduction.rst‎

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Imbalanced-learn samplers accept the same inputs that in scikit-learn:
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* ``data``: array-like (2-D list, pandas.Dataframe, numpy.array) or sparse
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matrices;
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* ``targets``: array-like (1-D list, pandas.Series, numpy.array).
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* `data`:
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* 2-D :class:`list`,
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* 2-D :class:`numpy.ndarray`,
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* :class:`pandas.DataFrame`,
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* :class:`scipy.sparse.csr_matrix` or :class:`scipy.sparse.csc_matrix`;
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* `targets`:
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* 1-D :class:`numpy.ndarray`,
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* :class:`pandas.Series`.
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* ``data_resampled``: array-like (2-D list, pandas.Dataframe, numpy.array) or
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sparse matrices;
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* ``targets_resampled``: 1-D numpy.array or pd.Series.
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* `data_resampled`:
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* 2-D :class:`numpy.ndarray`,
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* :class:`pandas.DataFrame`,
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* :class:`scipy.sparse.csr_matrix` or :class:`scipy.sparse.csc_matrix`;
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* `targets_resampled`:
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* 1-D :class:`numpy.ndarray`,
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* :class:`pandas.Series`.
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.. topic:: Pandas in/out
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Unlike scikit-learn, imbalanced-learn provides support for pandas in/out.
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Therefore providing a dataframe, will output as well a dataframe.
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.. topic:: Sparse input
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‎doc/miscellaneous.rst‎

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We illustrate the use of such sampler to implement an outlier rejection
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estimator which can be easily used within a
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:ref:`sphx_glr_auto_examples_plot_outlier_rejections.py`
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:ref:`sphx_glr_auto_examples_applications_plot_outlier_rejections.py`
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.. _generators:
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‎doc/references/index.rst‎

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.. _api:
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#############
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API reference
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pipeline
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metrics
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datasets
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Utilities
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utils

‎imblearn/base.py‎

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Notes
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:ref:`sphx_glr_auto_examples_applications_plot_outlier_rejections.py`
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‎imblearn/datasets/_imbalance.py‎

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:ref:`sphx_glr_auto_examples_api_plot_sampling_strategy_usage.py`.
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--------

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