:orphan:

Complex Models
==============

Examples that show how to build complex models.



.. raw:: html

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

.. thumbnail-parent-div-open

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example uses the iris dataset and tests a regression model in which the target variable is generated from some features within the cross-validation procedure. We will use the Iris dataset and generate a target variable using PCA on the petal features. Then, we will evaluate if a regression model can predict the generated target from the sepal features">

.. only:: html

  .. image:: /auto_examples/03_complex_models/images/thumb/sphx_glr_run_generate_target_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_03_complex_models_run_generate_target.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Target Generation</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example uses the sklearn diabetes regression dataset, and transforms the target variable, in this case, using z-score. Then, we perform a regression analysis using Ridge Regression model.">

.. only:: html

  .. image:: /auto_examples/03_complex_models/images/thumb/sphx_glr_run_apply_to_target_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_03_complex_models_run_apply_to_target.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Transforming target variable with z-score</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example uses the fmri dataset, performs simple binary classification using a Support Vector Machine classifier while tuning multiple hyperparameters grids at the same time.">

.. only:: html

  .. image:: /auto_examples/03_complex_models/images/thumb/sphx_glr_run_hyperparameter_multiple_grids_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_03_complex_models_run_hyperparameter_multiple_grids.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Tuning Multiple Hyperparameters Grids</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example uses the fmri dataset, performs simple binary classification using a Support Vector Machine classifier and analyzes the model.">

.. only:: html

  .. image:: /auto_examples/03_complex_models/images/thumb/sphx_glr_run_hyperparameter_tuning_bayessearch_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_03_complex_models_run_hyperparameter_tuning_bayessearch.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Tuning Hyperparameters using Bayesian Search</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example uses the iris dataset and performs a complex stacking classification. We will use two different classifiers, one applied to petal features and one applied to sepal features. A final logistic regression classifier will be applied on the predictions of the two classifiers.">

.. only:: html

  .. image:: /auto_examples/03_complex_models/images/thumb/sphx_glr_run_stacked_models_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_03_complex_models_run_stacked_models.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Stacking Classification</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example uses the fmri dataset, performs simple binary classification using a Support Vector Machine classifier and analyze the model.">

.. only:: html

  .. image:: /auto_examples/03_complex_models/images/thumb/sphx_glr_run_hyperparameter_tuning_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_03_complex_models_run_hyperparameter_tuning.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Tuning Hyperparameters</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This example uses the diabetes data from sklearn datasets and performs a regression analysis using a Ridge Regression model. We&#x27;ll use the julearn.PipelineCreator to create a pipeline with two different PCA steps and reduce the dimensionality of the data, each one computed on a different subset of features.">

.. only:: html

  .. image:: /auto_examples/03_complex_models/images/thumb/sphx_glr_run_example_pca_featsets_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_03_complex_models_run_example_pca_featsets.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Regression Analysis</div>
    </div>


.. thumbnail-parent-div-close

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/03_complex_models/run_generate_target
   /auto_examples/03_complex_models/run_apply_to_target
   /auto_examples/03_complex_models/run_hyperparameter_multiple_grids
   /auto_examples/03_complex_models/run_hyperparameter_tuning_bayessearch
   /auto_examples/03_complex_models/run_stacked_models
   /auto_examples/03_complex_models/run_hyperparameter_tuning
   /auto_examples/03_complex_models/run_example_pca_featsets



.. only:: html

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

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_