包含中文文档和官方原版，用markdown文件写成，如有需要，可以下载阅读markdown文件的软件，已经分享。

# 示例 英文地址: <https://scikit-learn.org/stable/auto_examples/index.html> ## 其他示例 scikit-learn 的 Miscellaneous 和入门示例。 | | | | | | -- | -- | -- | -- | |  <br/> [紧凑的估计表示](https://scikit-learn.org/stable/auto_examples/plot_changed_only_pprint_parameter.html#sphx-glr-auto-examples-plot-changed-only-pprint-parameter-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id2) |  <br/> [带有可视化API的ROC曲线](https://scikit-learn.org/stable/auto_examples/plot_roc_curve_visualization_api.html#sphx-glr-auto-examples-plot-roc-curve-visualization-api-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id3) |  <br/> [序回归](https://scikit-learn.org/stable/auto_examples/plot_isotonic_regression.html#sphx-glr-auto-examples-plot-isotonic-regression-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id4) |  <br/> [先进的绘图具有部分依赖](https://scikit-learn.org/stable/auto_examples/plot_partial_dependence_visualization_api.html#sphx-glr-auto-examples-plot-partial-dependence-visualization-api-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id5) | |  <br/> [使用多输出估计器完成人脸](https://scikit-learn.org/stable/auto_examples/plot_multioutput_face_completion.html#sphx-glr-auto-examples-plot-multioutput-face-completion-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id6) |  <br/> [多标签分类](https://scikit-learn.org/stable/auto_examples/plot_multilabel.html#sphx-glr-auto-examples-plot-multilabel-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id7) |  <br/> [比较异常检测算法以对玩具数据集进行异常检测](https://scikit-learn.org/stable/auto_examples/plot_anomaly_comparison.html#sphx-glr-auto-examples-plot-anomaly-comparison-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id8) |  <br/> [具有随机投影嵌入的Johnson-Lindenstrauss边界](https://scikit-learn.org/stable/auto_examples/plot_johnson_lindenstrauss_bound.html#sphx-glr-auto-examples-plot-johnson-lindenstrauss-bound-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id9) | |  <br/> [内核岭回归和SVR的比较](https://scikit-learn.org/stable/auto_examples/plot_kernel_ridge_regression.html#sphx-glr-auto-examples-plot-kernel-ridge-regression-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id10) |  <br/> [RBF内核的显式特征图逼近](https://scikit-learn.org/stable/auto_examples/plot_kernel_approximation.html#sphx-glr-auto-examples-plot-kernel-approximation-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id11) | ## 双聚类 有关`sklearn.cluster.bicluster`模块的示例。 | | | | | | -- | -- | -- | -- | |  <br/> [频谱共聚算法演示](Biclustering/a_demo_of_the_spectral_co-clustering_algorithm.md) |  <br/> [频谱双聚类算法的演示](Biclustering/a_demo_of_the_spectral_clustering_algorithm.md) |  <br/> [使用频谱共聚算法对文档进行聚合](Biclustering/biclustering_documents_with_the_spectral_co-clustering_algorithm.md) || ## 校准 举例说明了对分类器的预测概率进行校准的示例。 | | | | | | -- | -- | -- | -- | |  <br/> [分类器校准的比较](https://scikit-learn.org/stable/auto_examples/calibration/plot_compare_calibration.html#sphx-glr-auto-examples-calibration-plot-compare-calibration-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id15) |  <br/> [概率校准曲线](https://scikit-learn.org/stable/auto_examples/calibration/plot_calibration_curve.html#sphx-glr-auto-examples-calibration-plot-calibration-curve-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id16) |  <br/> [分类器的概率校准](https://scikit-learn.org/stable/auto_examples/calibration/plot_calibration.html#sphx-glr-auto-examples-calibration-plot-calibration-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id17) |  <br/> [3级分类的概率校准](https://scikit-learn.org/stable/auto_examples/calibration/plot_calibration_multiclass.html#sphx-glr-auto-examples-calibration-plot-calibration-multiclass-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id18) | ## 分类 有关分类算法的一般示例。 | | | | | | -- | -- | -- | -- | |  <br/> [分类法线和收缩线线性判别分析](https://scikit-learn.org/stable/auto_examples/classification/plot_lda.html#sphx-glr-auto-examples-classification-plot-lda-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id19) |  <br/> [识别手写数字](https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html#sphx-glr-auto-examples-classification-plot-digits-classification-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id20) |  <br/> [情节分类概率](https://scikit-learn.org/stable/auto_examples/classification/plot_classification_probability.html#sphx-glr-auto-examples-classification-plot-classification-probability-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id21) |  <br/> [分类器比较](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html#sphx-glr-auto-examples-classification-plot-classifier-comparison-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id22) | |  <br/> [线性和二次判别分析与协方差椭球](https://scikit-learn.org/stable/auto_examples/classification/plot_lda_qda.html#sphx-glr-auto-examples-classification-plot-lda-qda-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id23) | ## 多聚类 有关[`sklearn.cluster`](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.cluster "sklearn.cluster")模块的示例。 | | | | | | -- | -- | -- | -- | |  <br/> [绘制层次聚类树状图](https://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_dendrogram.html#sphx-glr-auto-examples-cluster-plot-agglomerative-dendrogram-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id24) |  <br/> [功能集聚](https://scikit-learn.org/stable/auto_examples/cluster/plot_digits_agglomeration.html#sphx-glr-auto-examples-cluster-plot-digits-agglomeration-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id25) |  <br/> [均值漂移聚类算法的演示](https://scikit-learn.org/stable/auto_examples/cluster/plot_mean_shift.html#sphx-glr-auto-examples-cluster-plot-mean-shift-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id26) |  <br/> [的k均值假设示范](https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_assumptions.html#sphx-glr-auto-examples-c