:orphan:

Confounds
=========

Examples that show the confound-related functionality of ``julearn``.



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    <div class="sphx-glr-thumbcontainer" tooltip="In most cases confound removal is a simple operation. You regress out the confound from the features and only continue working with these new confound removed features. This is also the default setting for julearn&#x27;s remove_confound step. But sometimes you want to work with the confound even after removing it from the features. In this example, we will discuss the options you have.">

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  .. image:: /auto_examples/04_confounds/images/thumb/sphx_glr_run_return_confounds_thumb.png
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  :ref:`sphx_glr_auto_examples_04_confounds_run_return_confounds.py`

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      <div class="sphx-glr-thumbnail-title">Return Confounds in Confound Removal</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example uses the iris dataset, performs simple binary classification with and without confound removal using a Random Forest classifier.">

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  .. image:: /auto_examples/04_confounds/images/thumb/sphx_glr_plot_confound_removal_classification_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_04_confounds_plot_confound_removal_classification.py`

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      <div class="sphx-glr-thumbnail-title">Confound Removal (model comparison)</div>
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.. toctree::
   :hidden:

   /auto_examples/04_confounds/run_return_confounds
   /auto_examples/04_confounds/plot_confound_removal_classification



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 .. rst-class:: sphx-glr-signature

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