.. DO NOT EDIT. .. 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" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` 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 14-65 .. image:: /auto_examples/linear_model/images/sphx_glr_plot_huber_vs_ridge_001.png :alt: Comparison of HuberRegressor vs Ridge :class: sphx-glr-single-img .. code-block:: default # Authors: Manoj Kumar mks542@nyu.edu # License: BSD 3 clause print(__doc__) import numpy as np import matplotlib.pyplot as plt 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. X_outliers[2:, :] += X.min() - X.mean() / 4. y_outliers[:2] += y.min() - y.mean() / 4. y_outliers[2:] += y.max() + y.mean() / 4. 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 = [1.35, 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, normalize=True) 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 0.163 seconds) .. _sphx_glr_download_auto_examples_linear_model_plot_huber_vs_ridge.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.24.X?urlpath=lab/tree/notebooks/auto_examples/linear_model/plot_huber_vs_ridge.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_huber_vs_ridge.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_huber_vs_ridge.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_