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.. _classification_examples:

All classification examples
============================

Following is a collection of notebooks demonstrating how to use MAPIE in classification.


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

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


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    <div class="sphx-glr-thumbcontainer" tooltip="In this example, we explain how to use MAPIE on a basic classification setting.">

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  :ref:`sphx_glr_examples_classification_1-quickstart_plot_quickstart_classification.py`

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      <div class="sphx-glr-thumbnail-title">Use MAPIE to plot prediction sets</div>
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2. Advanced analysis
--------------------

The following examples use MAPIE for discussing more complex MAPIE classification problems.


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    <div class="sphx-glr-thumbcontainer" tooltip="In this tutorial, we compare the prediction sets estimated by SplitConformalClassifier with the &quot;lac&quot; and &quot;aps&quot; on the two-dimensional dataset presented by Sadinle et al. (2019).">

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  :ref:`sphx_glr_examples_classification_2-advanced-analysis_plot_comp_methods_on_2d_dataset.py`

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      <div class="sphx-glr-thumbnail-title">LAC and APS methods explained</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="In this example, we propose set prediction for binary classification estimated by SplitConformalClassifier with the &quot;lac&quot; method on two-dimensional dataset.">

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  :ref:`sphx_glr_examples_classification_2-advanced-analysis_plot_main-tutorial-binary-classification.py`

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      <div class="sphx-glr-thumbnail-title">Set prediction example in the binary classification setting</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="In this tutorial, we estimate the impact of the training/conformalization split on the prediction sets and on the resulting coverage estimated by SplitConformalClassifier. We then adopt a cross-validation approach in which the conformity scores of all conformalization sets are used to estimate the quantile. We demonstrate that this second &quot;cross-conformal&quot; approach gives more robust prediction sets with accurate conformity plots.">

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  :ref:`sphx_glr_examples_classification_2-advanced-analysis_plot_crossconformal.py`

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      <div class="sphx-glr-thumbnail-title">Cross conformal classification explained</div>
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3. Simulations from scientific articles
---------------------------------------

The following examples reproduce the simulations from the scientific
articles that present the methods implemented
in MAPIE for classification settings.


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    <div class="sphx-glr-thumbcontainer" tooltip="We use SplitConformalClassifier to reproduce Example 7 from Sadinle et al. (2019).">

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  :ref:`sphx_glr_examples_classification_3-scientific-articles_plot_sadinle2019_example.py`

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      <div class="sphx-glr-thumbnail-title">Least Ambiguous Set-Valued Classifiers with Bounded Error Levels, Sadinle et al. (2019)</div>
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4. Other notebooks
--------------------

This section lists a series of Jupyter notebooks hosted on the MAPIE Github repository that can be run on Google Colab:

  - `Deep learning with MAPIE on the Cifar10 dataset <https://github.com/scikit-learn-contrib/MAPIE/tree/master/notebooks/classification/Cifar10.ipynb>`_


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   /examples_classification/1-quickstart/index.rst
   /examples_classification/2-advanced-analysis/index.rst
   /examples_classification/3-scientific-articles/index.rst
   /examples_classification/4-other-notebooks/index.rst


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      :download:`Download all examples in Python source code: examples_classification_python.zip </examples_classification/examples_classification_python.zip>`

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      :download:`Download all examples in Jupyter notebooks: examples_classification_jupyter.zip </examples_classification/examples_classification_jupyter.zip>`


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