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|rss_image|  **2018-05 - 1/1** :ref:`Blog <ap-main-0>` :ref:`machine_learning (5) <ap-cat-machine_learning-0>`

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.. index:: 2018-05


.. _ap-month-2018-05-0:

2018-05 - 1/1
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.. blogpostagg::
    :title: Quantile regression with scikit-learn.
    :date: 2018-05-07
    :keywords: scikit-learn,quantile regression
    :categories: machine learning
    :rawfile: 2018/2018-05-07_quantile_regression.rst

    :epkg:`scikit-learn` does not have any quantile regression.
    :epkg:`statsmodels` does have one
    `QuantReg <http://www.statsmodels.org/dev/generated/statsmodels.regression.quantile_regression.QuantReg.html>`_
    but I wanted to try something I did for my teachings
    `Régression Quantile
    <http://www.xavierdupre.fr/app/ensae_teaching_cs/helpsphinx/notebooks/td_note_2017_2.html?highlight=mediane>`_
    based on `Iteratively reweighted least squares
    <https://en.wikipedia.org/wiki/Iteratively_reweighted_least_squares>`_.
    I thought it was a good case study to turn a simple algorithm into
    a learner :epkg:`scikit-learn` can reused in a pipeline.
    The notebook :ref:`quantileregressionrst` demonstrates it
    and it is implemented in
    :class:`QuantileLinearRegression <mlinsights.mlmodel.quantile_regression.QuantileLinearRegression>`.





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|rss_image|  **2018-05 - 1/1** :ref:`2018-05 (1) <ap-month-2018-05-0>` :ref:`2019-02 (4) <ap-month-2019-02-0>` :ref:`2019-03 (1) <ap-month-2019-03-0>` :ref:`2020-09 (1) <ap-month-2020-09-0>` :ref:`2021-01 (1) <ap-month-2021-01-0>`