.. _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 :align: center .. code-block:: python # Author: Manoj Kumar # 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() # 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() **Total running time of the script:** (0 minutes 0.105 seconds) .. container:: sphx-glr-download **Download Python source code:** :download:`plot_select_from_model_boston.py ` .. container:: sphx-glr-download **Download IPython notebook:** :download:`plot_select_from_model_boston.ipynb `