.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_packages_scikit-learn_auto_examples_plot_separator.py: Simple picture of the formal problem of machine learning ========================================================= This example generates simple synthetic data ploints and shows a separating hyperplane on them. .. image:: /packages/scikit-learn/auto_examples/images/sphx_glr_plot_separator_001.png :class: sphx-glr-single-img .. code-block:: python import numpy as np import matplotlib.pyplot as plt from sklearn.linear_model import SGDClassifier from sklearn.datasets.samples_generator import make_blobs # we create 50 separable synthetic points X, Y = make_blobs(n_samples=50, centers=2, random_state=0, cluster_std=0.60) # fit the model clf = SGDClassifier(loss="hinge", alpha=0.01, fit_intercept=True) clf.fit(X, Y) # plot the line, the points, and the nearest vectors to the plane xx = np.linspace(-1, 5, 10) yy = np.linspace(-1, 5, 10) X1, X2 = np.meshgrid(xx, yy) Z = np.empty(X1.shape) for (i, j), val in np.ndenumerate(X1): x1 = val x2 = X2[i, j] p = clf.decision_function([[x1, x2]]) Z[i, j] = p[0] plt.figure(figsize=(4, 3)) ax = plt.axes() ax.contour(X1, X2, Z, [-1.0, 0.0, 1.0], colors='k', linestyles=['dashed', 'solid', 'dashed']) ax.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired) ax.axis('tight') plt.show() **Total running time of the script:** ( 0 minutes 0.026 seconds) .. _sphx_glr_download_packages_scikit-learn_auto_examples_plot_separator.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download :download:`Download Python source code: plot_separator.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_separator.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_