.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/linear_model/plot_logistic_path.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_logistic_path.py: ============================================== Regularization path of L1- Logistic Regression ============================================== Train l1-penalized logistic regression models on a binary classification problem derived from the Iris dataset. The models are ordered from strongest regularized to least regularized. The 4 coefficients of the models are collected and plotted as a "regularization path": on the left-hand side of the figure (strong regularizers), all the coefficients are exactly 0. When regularization gets progressively looser, coefficients can get non-zero values one after the other. Here we choose the liblinear solver because it can efficiently optimize for the Logistic Regression loss with a non-smooth, sparsity inducing l1 penalty. Also note that we set a low value for the tolerance to make sure that the model has converged before collecting the coefficients. We also use warm_start=True which means that the coefficients of the models are reused to initialize the next model fit to speed-up the computation of the full-path. .. GENERATED FROM PYTHON SOURCE LINES 28-77 .. image:: /auto_examples/linear_model/images/sphx_glr_plot_logistic_path_001.png :alt: Logistic Regression Path :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Computing regularization path ... This took 0.074s | .. code-block:: default print(__doc__) # Author: Alexandre Gramfort # License: BSD 3 clause from time import time import numpy as np import matplotlib.pyplot as plt from sklearn import linear_model from sklearn import datasets from sklearn.svm import l1_min_c iris = datasets.load_iris() X = iris.data y = iris.target X = X[y != 2] y = y[y != 2] X /= X.max() # Normalize X to speed-up convergence # ############################################################################# # Demo path functions cs = l1_min_c(X, y, loss='log') * np.logspace(0, 7, 16) print("Computing regularization path ...") start = time() clf = linear_model.LogisticRegression(penalty='l1', solver='liblinear', tol=1e-6, max_iter=int(1e6), warm_start=True, intercept_scaling=10000.) coefs_ = [] for c in cs: clf.set_params(C=c) clf.fit(X, y) coefs_.append(clf.coef_.ravel().copy()) print("This took %0.3fs" % (time() - start)) coefs_ = np.array(coefs_) plt.plot(np.log10(cs), coefs_, marker='o') ymin, ymax = plt.ylim() plt.xlabel('log(C)') plt.ylabel('Coefficients') plt.title('Logistic Regression Path') plt.axis('tight') plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.185 seconds) .. _sphx_glr_download_auto_examples_linear_model_plot_logistic_path.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_logistic_path.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_logistic_path.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_logistic_path.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_