.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/linear_model/plot_ols_3d.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_linear_model_plot_ols_3d.py: ========================================================= Sparsity Example: Fitting only features 1 and 2 ========================================================= Features 1 and 2 of the diabetes-dataset are fitted and plotted below. It illustrates that although feature 2 has a strong coefficient on the full model, it does not give us much regarding `y` when compared to just feature 1. .. GENERATED FROM PYTHON SOURCE LINES 11-16 .. code-block:: Python # Code source: Gaƫl Varoquaux # Modified for documentation by Jaques Grobler # License: BSD 3 clause .. GENERATED FROM PYTHON SOURCE LINES 17-18 First we load the diabetes dataset. .. GENERATED FROM PYTHON SOURCE LINES 18-31 .. code-block:: Python import numpy as np from sklearn import datasets X, y = datasets.load_diabetes(return_X_y=True) indices = (0, 1) X_train = X[:-20, indices] X_test = X[-20:, indices] y_train = y[:-20] y_test = y[-20:] .. GENERATED FROM PYTHON SOURCE LINES 32-33 Next we fit a linear regression model. .. GENERATED FROM PYTHON SOURCE LINES 33-40 .. code-block:: Python from sklearn import linear_model ols = linear_model.LinearRegression() _ = ols.fit(X_train, y_train) .. GENERATED FROM PYTHON SOURCE LINES 41-42 Finally we plot the figure from three different views. .. GENERATED FROM PYTHON SOURCE LINES 42-85 .. code-block:: Python import matplotlib.pyplot as plt # unused but required import for doing 3d projections with matplotlib < 3.2 import mpl_toolkits.mplot3d # noqa: F401 def plot_figs(fig_num, elev, azim, X_train, clf): fig = plt.figure(fig_num, figsize=(4, 3)) plt.clf() ax = fig.add_subplot(111, projection="3d", elev=elev, azim=azim) ax.scatter(X_train[:, 0], X_train[:, 1], y_train, c="k", marker="+") ax.plot_surface( np.array([[-0.1, -0.1], [0.15, 0.15]]), np.array([[-0.1, 0.15], [-0.1, 0.15]]), clf.predict( np.array([[-0.1, -0.1, 0.15, 0.15], [-0.1, 0.15, -0.1, 0.15]]).T ).reshape((2, 2)), alpha=0.5, ) ax.set_xlabel("X_1") ax.set_ylabel("X_2") ax.set_zlabel("Y") ax.xaxis.set_ticklabels([]) ax.yaxis.set_ticklabels([]) ax.zaxis.set_ticklabels([]) # Generate the three different figures from different views elev = 43.5 azim = -110 plot_figs(1, elev, azim, X_train, ols) elev = -0.5 azim = 0 plot_figs(2, elev, azim, X_train, ols) elev = -0.5 azim = 90 plot_figs(3, elev, azim, X_train, ols) plt.show() .. rst-class:: sphx-glr-horizontal * .. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_ols_3d_001.png :alt: plot ols 3d :srcset: /auto_examples/linear_model/images/sphx_glr_plot_ols_3d_001.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_ols_3d_002.png :alt: plot ols 3d :srcset: /auto_examples/linear_model/images/sphx_glr_plot_ols_3d_002.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_ols_3d_003.png :alt: plot ols 3d :srcset: /auto_examples/linear_model/images/sphx_glr_plot_ols_3d_003.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.168 seconds) .. _sphx_glr_download_auto_examples_linear_model_plot_ols_3d.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/linear_model/plot_ols_3d.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/?path=auto_examples/linear_model/plot_ols_3d.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_ols_3d.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_ols_3d.py ` .. include:: plot_ols_3d.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_