.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/classification/plot_classifier_comparison.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_classification_plot_classifier_comparison.py: ===================== 分类器比较 ===================== 在scikit-learn中的一些分类器在合成数据集上的比较。 这个示例的目的是为了展示不同分类器的决策边界的性质。 需要谨慎看待这些示例所传达的直观感受，因为这些直观感受不一定适用于真实数据集。 特别是在高维空间中，数据更容易线性分离，像朴素贝叶斯和线性SVM这样的简单分类器可能会比其他分类器更好地实现泛化。 图中显示了训练点（实心颜色）和测试点（半透明颜色）。右下角显示了测试集上的分类准确率。 .. GENERATED FROM PYTHON SOURCE LINES 15-154 .. image-sg:: /auto_examples/classification/images/sphx_glr_plot_classifier_comparison_001.png :alt: Input data, Nearest Neighbors, Linear SVM, RBF SVM, Gaussian Process, Decision Tree, Random Forest, Neural Net, AdaBoost, Naive Bayes, QDA :srcset: /auto_examples/classification/images/sphx_glr_plot_classifier_comparison_001.png :class: sphx-glr-single-img .. code-block:: Python # 代码来源：Gaël Varoquaux # Andreas Müller # 由Jaques Grobler为文档修改 # SPDX许可证标识符：BSD-3-Clause import matplotlib.pyplot as plt import numpy as np from matplotlib.colors import ListedColormap from sklearn.datasets import make_circles, make_classification, make_moons from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier from sklearn.gaussian_process import GaussianProcessClassifier from sklearn.gaussian_process.kernels import RBF from sklearn.inspection import DecisionBoundaryDisplay from sklearn.model_selection import train_test_split from sklearn.naive_bayes import GaussianNB from sklearn.neighbors import KNeighborsClassifier from sklearn.neural_network import MLPClassifier from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler from sklearn.svm import SVC from sklearn.tree import DecisionTreeClassifier names = [ "Nearest Neighbors", "Linear SVM", "RBF SVM", "Gaussian Process", "Decision Tree", "Random Forest", "Neural Net", "AdaBoost", "Naive Bayes", "QDA", ] classifiers = [ KNeighborsClassifier(3), SVC(kernel="linear", C=0.025, random_state=42), SVC(gamma=2, C=1, random_state=42), GaussianProcessClassifier(1.0 * RBF(1.0), random_state=42), DecisionTreeClassifier(max_depth=5, random_state=42), RandomForestClassifier( max_depth=5, n_estimators=10, max_features=1, random_state=42 ), MLPClassifier(alpha=1, max_iter=1000, random_state=42), AdaBoostClassifier(algorithm="SAMME", random_state=42), GaussianNB(), QuadraticDiscriminantAnalysis(), ] X, y = make_classification( n_features=2, n_redundant=0, n_informative=2, random_state=1, n_clusters_per_class=1 ) rng = np.random.RandomState(2) X += 2 * rng.uniform(size=X.shape) linearly_separable = (X, y) datasets = [ make_moons(noise=0.3, random_state=0), make_circles(noise=0.2, factor=0.5, random_state=1), linearly_separable, ] figure = plt.figure(figsize=(27, 9)) i = 1 # 遍历数据集 for ds_cnt, ds in enumerate(datasets): # 预处理数据集，分为训练部分和测试部分 X, y = ds X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.4, random_state=42 ) x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5 y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5 # 只需先绘制数据集。 cm = plt.cm.RdBu cm_bright = ListedColormap(["#FF0000", "#0000FF"]) ax = plt.subplot(len(datasets), len(classifiers) + 1, i) if ds_cnt == 0: ax.set_title("Input data") # 绘制训练点 ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k") # 绘制测试点 ax.scatter( X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6, edgecolors="k" ) ax.set_xlim(x_min, x_max) ax.set_ylim(y_min, y_max) ax.set_xticks(()) ax.set_yticks(()) i += 1 # 遍历分类器 for name, clf in zip(names, classifiers): ax = plt.subplot(len(datasets), len(classifiers) + 1, i) clf = make_pipeline(StandardScaler(), clf) clf.fit(X_train, y_train) score = clf.score(X_test, y_test) DecisionBoundaryDisplay.from_estimator( clf, X, cmap=cm, alpha=0.8, ax=ax, eps=0.5 ) # 绘制训练点 ax.scatter( X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k" ) # 绘制测试点 ax.scatter( X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, edgecolors="k", alpha=0.6, ) ax.set_xlim(x_min, x_max) ax.set_ylim(y_min, y_max) ax.set_xticks(()) ax.set_yticks(()) if ds_cnt == 0: ax.set_title(name) ax.text( x_max - 0.3, y_min + 0.3, ("%.2f" % score).lstrip("0"), size=15, horizontalalignment="right", ) i += 1 plt.tight_layout() plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 1.112 seconds) .. _sphx_glr_download_auto_examples_classification_plot_classifier_comparison.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/classification/plot_classifier_comparison.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_classifier_comparison.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_classifier_comparison.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_classifier_comparison.zip ` .. include:: plot_classifier_comparison.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_