

.. _sphx_glr_auto_examples_applications:

.. _realword_examples:

Examples based on real world datasets
-------------------------------------

Examples which use real-word dataset.



.. raw:: html

    <div class="sphx-glr-thumbnails">

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    <div class="sphx-glr-thumbcontainer" tooltip="Some balancing methods allow for balancing dataset with multiples classes. We provide an example to illustrate the use of those methods which do not differ from the binary case.">

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  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_multi_class_under_sampling_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_multi_class_under_sampling`

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      <div class="sphx-glr-thumbnail-title">Multiclass classification with under-sampling</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to balance the text data before to train a classifier.">

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  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_topic_classication_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_topic_classication`

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      <div class="sphx-glr-thumbnail-title">Example of topic classification in text documents</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="In this face recognition example two faces are used from the LFW (Faces in the Wild) dataset. Several implemented over-sampling methods are used in conjunction with a 3NN classifier in order to examine the improvement of the classifier&#x27;s output quality by using an over-sampler.">

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  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_over_sampling_benchmark_lfw_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_over_sampling_benchmark_lfw`

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      <div class="sphx-glr-thumbnail-title">Benchmark over-sampling methods in a face recognition task</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the use of a custom sampler to implement an outlier rejections estimator. It can be used easily within a pipeline in which the number of samples can vary during training, which usually is a limitation of the current scikit-learn pipeline.">

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  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_outlier_rejections_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_outlier_rejections`

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      <div class="sphx-glr-thumbnail-title">Customized sampler to implement an outlier rejections estimator</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="This example illustrates the problem induced by learning on datasets having imbalanced classes. Subsequently, we compare different approaches alleviating these negative effects.">

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  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_impact_imbalanced_classes_thumb.png
    :alt:

  :doc:`/auto_examples/applications/plot_impact_imbalanced_classes`

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      <div class="sphx-glr-thumbnail-title">Fitting model on imbalanced datasets and how to fight bias</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="This example compares two strategies to train a neural-network on the Porto Seguro Kaggle data set [1]_. The data set is imbalanced and we show that balancing each mini-batch allows to improve performance and reduce the training time.">

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  .. image:: /auto_examples/applications/images/thumb/sphx_glr_porto_seguro_keras_under_sampling_thumb.png
    :alt:

  :doc:`/auto_examples/applications/porto_seguro_keras_under_sampling`

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      <div class="sphx-glr-thumbnail-title">Porto Seguro: balancing samples in mini-batches with Keras</div>
    </div>


.. thumbnail-parent-div-close

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    </div>


.. toctree::
   :hidden:

   /auto_examples/applications/plot_multi_class_under_sampling
   /auto_examples/applications/plot_topic_classication
   /auto_examples/applications/plot_over_sampling_benchmark_lfw
   /auto_examples/applications/plot_outlier_rejections
   /auto_examples/applications/plot_impact_imbalanced_classes
   /auto_examples/applications/porto_seguro_keras_under_sampling

