.. _sphx_glr_examples_regression_1-quickstart:

.. _regression_examples_1:

1. Quickstart
----------------------

The following examples present the main functionalities of MAPIE through basic quickstart regression problems.


.. raw:: html

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

.. thumbnail-parent-div-open

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Plot prediction intervals">

.. only:: html

  .. image:: /examples_regression/1-quickstart/images/thumb/sphx_glr_plot_toy_model_thumb.png
    :alt:

  :ref:`sphx_glr_examples_regression_1-quickstart_plot_toy_model.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Plot prediction intervals</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip=" This example uses CrossConformalRegressor to estimate prediction intervals associated with Gamma distributed target. The limit of the absolute residual conformity score is illustrated.">

.. only:: html

  .. image:: /examples_regression/1-quickstart/images/thumb/sphx_glr_plot_compare_conformity_scores_thumb.png
    :alt:

  :ref:`sphx_glr_examples_regression_1-quickstart_plot_compare_conformity_scores.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Data with gamma distribution</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip=" SplitConformalRegressor and ConformalizedQuantileRegressor are used to conformalize uncertainties for large models for which the cost of cross-validation is too high. Typically, neural networks rely on a single validation set.">

.. only:: html

  .. image:: /examples_regression/1-quickstart/images/thumb/sphx_glr_plot_prefit_thumb.png
    :alt:

  :ref:`sphx_glr_examples_regression_1-quickstart_plot_prefit.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Use a pre-trained model</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip=" CrossConformalRegressor, JackknifeAfterBootstrapRegressor, ConformalizedQuantileRegressor are used to estimate the prediction intervals of 1D heteroscedastic data using different strategies.">

.. only:: html

  .. image:: /examples_regression/1-quickstart/images/thumb/sphx_glr_plot_heteroscedastic_1d_data_thumb.png
    :alt:

  :ref:`sphx_glr_examples_regression_1-quickstart_plot_heteroscedastic_1d_data.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Data with uneven uncertainty</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip=" We show here how to use various MAPIE methods on data with homoscedastic data.">

.. only:: html

  .. image:: /examples_regression/1-quickstart/images/thumb/sphx_glr_plot_homoscedastic_1d_data_thumb.png
    :alt:

  :ref:`sphx_glr_examples_regression_1-quickstart_plot_homoscedastic_1d_data.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Data with constant uncertainty</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Note: in this tutorial, we use the following terms employed in the scientific literature:">

.. only:: html

  .. image:: /examples_regression/1-quickstart/images/thumb/sphx_glr_plot_ts-tutorial_thumb.png
    :alt:

  :ref:`sphx_glr_examples_regression_1-quickstart_plot_ts-tutorial.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Tutorial for time series</div>
    </div>


.. thumbnail-parent-div-close

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /examples_regression/1-quickstart/plot_toy_model
   /examples_regression/1-quickstart/plot_compare_conformity_scores
   /examples_regression/1-quickstart/plot_prefit
   /examples_regression/1-quickstart/plot_heteroscedastic_1d_data
   /examples_regression/1-quickstart/plot_homoscedastic_1d_data
   /examples_regression/1-quickstart/plot_ts-tutorial