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.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/linear_model/plot_huber_vs_ridge.py"
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.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_linear_model_plot_huber_vs_ridge.py>`
        to download the full example code. or to run this example in your browser via Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_linear_model_plot_huber_vs_ridge.py:


=======================================================
HuberRegressor vs Ridge on dataset with strong outliers
=======================================================

Fit Ridge and HuberRegressor on a dataset with outliers.

The example shows that the predictions in ridge are strongly influenced
by the outliers present in the dataset. The Huber regressor is less
influenced by the outliers since the model uses the linear loss for these.
As the parameter epsilon is increased for the Huber regressor, the decision
function approaches that of the ridge.

.. GENERATED FROM PYTHON SOURCE LINES 15-65



.. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_huber_vs_ridge_001.png
   :alt: Comparison of HuberRegressor vs Ridge
   :srcset: /auto_examples/linear_model/images/sphx_glr_plot_huber_vs_ridge_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    # Authors: The scikit-learn developers
    # SPDX-License-Identifier: BSD-3-Clause

    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn.datasets import make_regression
    from sklearn.linear_model import HuberRegressor, Ridge

    # Generate toy data.
    rng = np.random.RandomState(0)
    X, y = make_regression(
        n_samples=20, n_features=1, random_state=0, noise=4.0, bias=100.0
    )

    # Add four strong outliers to the dataset.
    X_outliers = rng.normal(0, 0.5, size=(4, 1))
    y_outliers = rng.normal(0, 2.0, size=4)
    X_outliers[:2, :] += X.max() + X.mean() / 4.0
    X_outliers[2:, :] += X.min() - X.mean() / 4.0
    y_outliers[:2] += y.min() - y.mean() / 4.0
    y_outliers[2:] += y.max() + y.mean() / 4.0
    X = np.vstack((X, X_outliers))
    y = np.concatenate((y, y_outliers))
    plt.plot(X, y, "b.")

    # Fit the huber regressor over a series of epsilon values.
    colors = ["r-", "b-", "y-", "m-"]

    x = np.linspace(X.min(), X.max(), 7)
    epsilon_values = [1, 1.5, 1.75, 1.9]
    for k, epsilon in enumerate(epsilon_values):
        huber = HuberRegressor(alpha=0.0, epsilon=epsilon)
        huber.fit(X, y)
        coef_ = huber.coef_ * x + huber.intercept_
        plt.plot(x, coef_, colors[k], label="huber loss, %s" % epsilon)

    # Fit a ridge regressor to compare it to huber regressor.
    ridge = Ridge(alpha=0.0, random_state=0)
    ridge.fit(X, y)
    coef_ridge = ridge.coef_
    coef_ = ridge.coef_ * x + ridge.intercept_
    plt.plot(x, coef_, "g-", label="ridge regression")

    plt.title("Comparison of HuberRegressor vs Ridge")
    plt.xlabel("X")
    plt.ylabel("y")
    plt.legend(loc=0)
    plt.show()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.092 seconds)


.. _sphx_glr_download_auto_examples_linear_model_plot_huber_vs_ridge.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

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        :alt: Launch binder
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.. include:: plot_huber_vs_ridge.recommendations


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

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