.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/svm/plot_weighted_samples.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_svm_plot_weighted_samples.py: ===================== SVM: Weighted samples ===================== Plot decision function of a weighted dataset, where the size of points is proportional to its weight. The sample weighting rescales the C parameter, which means that the classifier puts more emphasis on getting these points right. The effect might often be subtle. To emphasize the effect here, we particularly weight outliers, making the deformation of the decision boundary very visible. .. GENERATED FROM PYTHON SOURCE LINES 16-76 .. image-sg:: /auto_examples/svm/images/sphx_glr_plot_weighted_samples_001.png :alt: Constant weights, Modified weights :srcset: /auto_examples/svm/images/sphx_glr_plot_weighted_samples_001.png :class: sphx-glr-single-img .. code-block:: Python # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause import matplotlib.pyplot as plt import numpy as np from sklearn import svm def plot_decision_function(classifier, sample_weight, axis, title): # plot the decision function xx, yy = np.meshgrid(np.linspace(-4, 5, 500), np.linspace(-4, 5, 500)) Z = classifier.decision_function(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) # plot the line, the points, and the nearest vectors to the plane axis.contourf(xx, yy, Z, alpha=0.75, cmap=plt.cm.bone) axis.scatter( X[:, 0], X[:, 1], c=y, s=100 * sample_weight, alpha=0.9, cmap=plt.cm.bone, edgecolors="black", ) axis.axis("off") axis.set_title(title) # we create 20 points np.random.seed(0) X = np.r_[np.random.randn(10, 2) + [1, 1], np.random.randn(10, 2)] y = [1] * 10 + [-1] * 10 sample_weight_last_ten = abs(np.random.randn(len(X))) sample_weight_constant = np.ones(len(X)) # and bigger weights to some outliers sample_weight_last_ten[15:] *= 5 sample_weight_last_ten[9] *= 15 # Fit the models. # This model does not take into account sample weights. clf_no_weights = svm.SVC(gamma=1) clf_no_weights.fit(X, y) # This other model takes into account some dedicated sample weights. clf_weights = svm.SVC(gamma=1) clf_weights.fit(X, y, sample_weight=sample_weight_last_ten) fig, axes = plt.subplots(1, 2, figsize=(14, 6)) plot_decision_function( clf_no_weights, sample_weight_constant, axes[0], "Constant weights" ) plot_decision_function(clf_weights, sample_weight_last_ten, axes[1], "Modified weights") plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.547 seconds) .. _sphx_glr_download_auto_examples_svm_plot_weighted_samples.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/svm/plot_weighted_samples.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/index.html?path=auto_examples/svm/plot_weighted_samples.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_weighted_samples.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_weighted_samples.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_weighted_samples.zip ` .. include:: plot_weighted_samples.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_