.. 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_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
.. rst-class:: sphx-glr-horizontal
*
.. image:: /auto_examples/linear_model/images/sphx_glr_plot_ols_3d_001.png
:alt: plot ols 3d
:class: sphx-glr-multi-img
*
.. image:: /auto_examples/linear_model/images/sphx_glr_plot_ols_3d_002.png
:alt: plot ols 3d
:class: sphx-glr-multi-img
*
.. image:: /auto_examples/linear_model/images/sphx_glr_plot_ols_3d_003.png
:alt: plot ols 3d
:class: sphx-glr-multi-img
.. code-block:: default
print(__doc__)
# Code source: Gaƫl Varoquaux
# Modified for documentation by Jaques Grobler
# License: BSD 3 clause
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
from sklearn import datasets, linear_model
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:]
ols = linear_model.LinearRegression()
ols.fit(X_train, y_train)
# #############################################################################
# Plot the figure
def plot_figs(fig_num, elev, azim, X_train, clf):
fig = plt.figure(fig_num, figsize=(4, 3))
plt.clf()
ax = Axes3D(fig, elev=elev, azim=azim)
ax.scatter(X_train[:, 0], X_train[:, 1], y_train, c='k', marker='+')
ax.plot_surface(np.array([[-.1, -.1], [.15, .15]]),
np.array([[-.1, .15], [-.1, .15]]),
clf.predict(np.array([[-.1, -.1, .15, .15],
[-.1, .15, -.1, .15]]).T
).reshape((2, 2)),
alpha=.5)
ax.set_xlabel('X_1')
ax.set_ylabel('X_2')
ax.set_zlabel('Y')
ax.w_xaxis.set_ticklabels([])
ax.w_yaxis.set_ticklabels([])
ax.w_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 = -.5
azim = 0
plot_figs(2, elev, azim, X_train, ols)
elev = -.5
azim = 90
plot_figs(3, elev, azim, X_train, ols)
plt.show()
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 0.209 seconds)
.. _sphx_glr_download_auto_examples_linear_model_plot_ols_3d.py:
.. only :: html
.. container:: sphx-glr-footer
:class: sphx-glr-footer-example
.. container:: binder-badge
.. image:: https://mybinder.org/badge_logo.svg
:target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.23.X?urlpath=lab/tree/notebooks/auto_examples/linear_model/plot_ols_3d.ipynb
:width: 150 px
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_ols_3d.py `
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_ols_3d.ipynb `
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery `_