.. _sphx_glr_auto_examples_linear_model_plot_logistic_path.py: ================================= Path with L1- Logistic Regression ================================= Computes path on IRIS dataset. .. code-block:: python print(__doc__) # Author: Alexandre Gramfort # License: BSD 3 clause from datetime import datetime 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 -= np.mean(X, 0) Demo path functions .. code-block:: python cs = l1_min_c(X, y, loss='log') * np.logspace(0, 3) print("Computing regularization path ...") start = datetime.now() clf = linear_model.LogisticRegression(C=1.0, penalty='l1', tol=1e-6) coefs_ = [] for c in cs: clf.set_params(C=c) clf.fit(X, y) coefs_.append(clf.coef_.ravel().copy()) print("This took ", datetime.now() - start) coefs_ = np.array(coefs_) plt.plot(np.log10(cs), coefs_) ymin, ymax = plt.ylim() plt.xlabel('log(C)') plt.ylabel('Coefficients') plt.title('Logistic Regression Path') plt.axis('tight') plt.show() .. image:: /auto_examples/linear_model/images/sphx_glr_plot_logistic_path_001.png :align: center .. rst-class:: sphx-glr-script-out Out:: Computing regularization path ... This took 0:00:00.050203 **Total running time of the script:** (0 minutes 0.110 seconds) .. container:: sphx-glr-download **Download Python source code:** :download:`plot_logistic_path.py ` .. container:: sphx-glr-download **Download IPython notebook:** :download:`plot_logistic_path.ipynb `