.. _sphx_glr_auto_examples_use_cases:

End-to-end data science use cases
---------------------------------

These examples showcase skore in action on real use cases.
We aimed at showing skore's ability to:

- be compatible with scikit-learn
- reduce boilerplate code for some standard *de facto* data science analysis
- speed-up exploration by optimizing some internal computation



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

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    <div class="sphx-glr-thumbcontainer" tooltip="In this example, we tackle the California housing dataset where the goal is to perform a regression task: predicting house prices based on features such as the number of bedrooms, the geolocation, etc. For that, we try out several families of models. We evaluate these methods using skore&#x27;s EstimatorReport and its report on metrics.">

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  .. image:: /auto_examples/use_cases/images/thumb/sphx_glr_plot_feature_importance_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_use_cases_plot_feature_importance.py`

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      <div class="sphx-glr-thumbnail-title">EstimatorReport: Inspecting your models with the feature importance</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to leverage skore for structuring useful experiment information allowing to get insights from machine learning experiments.">

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  .. image:: /auto_examples/use_cases/images/thumb/sphx_glr_plot_employee_salaries_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_use_cases_plot_employee_salaries.py`

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      <div class="sphx-glr-thumbnail-title">Simplified and structured experiment reporting</div>
    </div>


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    </div>


.. toctree::
   :hidden:

   /auto_examples/use_cases/plot_feature_importance
   /auto_examples/use_cases/plot_employee_salaries