.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/svm/plot_separating_hyperplane_unbalanced.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 Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_svm_plot_separating_hyperplane_unbalanced.py: ================================================= SVM:用于不平衡类别的分离超平面 ================================================= 使用SVC为不平衡类别找到最优分离超平面。 我们首先使用普通的SVC找到分离平面,然后绘制(虚线)自动校正不平衡类别的分离超平面。 .. currentmodule:: sklearn.linear_model .. note:: 通过将 ``SVC(kernel="linear")`` 替换为 ``SGDClassifier(loss="hinge")`` ,此示例也能正常工作。将:class:`SGDClassifier` 的 ``loss`` 参数设置为 ``hinge`` 将产生类似于具有线性核的SVC的行为。 例如,可以尝试替换 ``SVC`` 为:: clf = SGDClassifier(n_iter=100, alpha=0.01) .. GENERATED FROM PYTHON SOURCE LINES 21-87 .. image-sg:: /auto_examples/svm/images/sphx_glr_plot_separating_hyperplane_unbalanced_001.png :alt: plot separating hyperplane unbalanced :srcset: /auto_examples/svm/images/sphx_glr_plot_separating_hyperplane_unbalanced_001.png :class: sphx-glr-single-img .. code-block:: Python import matplotlib.lines as mlines import matplotlib.pyplot as plt from sklearn import svm from sklearn.datasets import make_blobs from sklearn.inspection import DecisionBoundaryDisplay # 我们创建了两个随机点的簇 n_samples_1 = 1000 n_samples_2 = 100 centers = [[0.0, 0.0], [2.0, 2.0]] clusters_std = [1.5, 0.5] X, y = make_blobs( n_samples=[n_samples_1, n_samples_2], centers=centers, cluster_std=clusters_std, random_state=0, shuffle=False, ) # 拟合模型并获得分离超平面 clf = svm.SVC(kernel="linear", C=1.0) clf.fit(X, y) # 拟合模型并使用加权类别获得分离超平面 wclf = svm.SVC(kernel="linear", class_weight={1: 10}) wclf.fit(X, y) # 绘制样本 plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired, edgecolors="k") # 绘制两个分类器的决策函数 ax = plt.gca() disp = DecisionBoundaryDisplay.from_estimator( clf, X, plot_method="contour", colors="k", levels=[0], alpha=0.5, linestyles=["-"], ax=ax, ) # 为加权类别绘制决策边界和边距 wdisp = DecisionBoundaryDisplay.from_estimator( wclf, X, plot_method="contour", colors="r", levels=[0], alpha=0.5, linestyles=["-"], ax=ax, ) plt.legend( [ mlines.Line2D([], [], color="k", label="non weighted"), mlines.Line2D([], [], color="r", label="weighted"), ], ["non weighted", "weighted"], loc="upper right", ) plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.081 seconds) .. _sphx_glr_download_auto_examples_svm_plot_separating_hyperplane_unbalanced.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_separating_hyperplane_unbalanced.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_separating_hyperplane_unbalanced.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_separating_hyperplane_unbalanced.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_separating_hyperplane_unbalanced.zip ` .. include:: plot_separating_hyperplane_unbalanced.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_