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

    Click :ref:`here <sphx_glr_download_auto_examples_feature_selection_plot_select_from_model_boston.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_feature_selection_plot_select_from_model_boston.py:


===================================================
Feature selection using SelectFromModel and LassoCV
===================================================

Use SelectFromModel meta-transformer along with Lasso to select the best
couple of features from the Boston dataset.



.. image:: /auto_examples/feature_selection/images/sphx_glr_plot_select_from_model_boston_001.png
    :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none







|


.. code-block:: default

    # Author: Manoj Kumar <mks542@nyu.edu>
    # License: BSD 3 clause

    print(__doc__)

    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn.datasets import load_boston
    from sklearn.feature_selection import SelectFromModel
    from sklearn.linear_model import LassoCV

    # Load the boston dataset.
    boston = load_boston()
    X, y = boston['data'], boston['target']

    # We use the base estimator LassoCV since the L1 norm promotes sparsity of features.
    clf = LassoCV(cv=5)

    # Set a minimum threshold of 0.25
    sfm = SelectFromModel(clf, threshold=0.25)
    sfm.fit(X, y)
    n_features = sfm.transform(X).shape[1]

    # Reset the threshold till the number of features equals two.
    # Note that the attribute can be set directly instead of repeatedly
    # fitting the metatransformer.
    while n_features > 2:
        sfm.threshold += 0.1
        X_transform = sfm.transform(X)
        n_features = X_transform.shape[1]

    # Plot the selected two features from X.
    plt.title(
        "Features selected from Boston using SelectFromModel with "
        "threshold %0.3f." % sfm.threshold)
    feature1 = X_transform[:, 0]
    feature2 = X_transform[:, 1] 
    plt.plot(feature1, feature2, 'r.')
    plt.xlabel("Feature number 1")
    plt.ylabel("Feature number 2")
    plt.ylim([np.min(feature2), np.max(feature2)])
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.229 seconds)


.. _sphx_glr_download_auto_examples_feature_selection_plot_select_from_model_boston.py:


.. only :: html

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



  .. container:: sphx-glr-download

     :download:`Download Python source code: plot_select_from_model_boston.py <plot_select_from_model_boston.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: plot_select_from_model_boston.ipynb <plot_select_from_model_boston.ipynb>`


.. only:: html

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

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_