diff --git a/docs/examples/README.md b/docs/examples/README.md index 4662c1f240e9900585ca1d3c935be903b760e32d..d77d250a6376b12f9a240fa128b68ed5626cc149 100644 --- a/docs/examples/README.md +++ b/docs/examples/README.md @@ -2,18 +2,18 @@ 英文地址: -## Miscellaneous 示例 +## 杂项示例 scikit-learn 的 Miscellaneous 和入门示例。 | | | | | | -- | -- | -- | -- | -| ![](img/sphx_glr_plot_changed_only_pprint_parameter_thumb.png)
[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) | ![](img/sphx_glr_plot_roc_curve_visualization_api_thumb.png)
[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) | ![](img/sphx_glr_plot_isotonic_regression_thumb.png)
[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) | ![](img/sphx_glr_plot_partial_dependence_visualization_api_thumb.png)
[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) | -| ![](img/sphx_glr_plot_multioutput_face_completion_thumb.png)
[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) | ![](img/sphx_glr_plot_multilabel_thumb.png)
[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) | ![](img/sphx_glr_plot_anomaly_comparison_thumb.png)
[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) | ![](img/sphx_glr_plot_johnson_lindenstrauss_bound_thumb.png)
[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) | +| ![](img/sphx_glr_plot_changed_only_pprint_parameter_thumb.png)
[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) | ![](img/sphx_glr_plot_roc_curve_visualization_api_thumb.png)
[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) | ![](img/sphx_glr_plot_isotonic_regression_thumb.png)
[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) | ![](img/sphx_glr_plot_partial_dependence_visualization_api_thumb.png)
[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) | +| ![](img/sphx_glr_plot_multioutput_face_completion_thumb.png)
[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) | ![](img/sphx_glr_plot_multilabel_thumb.png)
[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) | ![](img/sphx_glr_plot_anomaly_comparison_thumb.png)
[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) | ![](img/sphx_glr_plot_johnson_lindenstrauss_bound_thumb.png)
[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) | | ![](img/sphx_glr_plot_kernel_ridge_regression_thumb.png)
[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) | ![](img/sphx_glr_plot_kernel_approximation_thumb.png)
[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) | -## 集群化 +## 双集群 有关`sklearn.cluster.bicluster`模块的示例。 @@ -37,22 +37,22 @@ scikit-learn 的 Miscellaneous 和入门示例。 | | | | | | -- | -- | -- | -- | -| ![](img/sphx_glr_plot_lda_thumb.png)
[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) | ![](img/sphx_glr_plot_digits_classification_thumb.png)
[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) | ![](img/sphx_glr_plot_classification_probability_thumb.png)
[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) | ![](img/sphx_glr_plot_classifier_comparison_thumb.png)
[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) | +| ![](img/sphx_glr_plot_lda_thumb.png)
[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) | ![](img/sphx_glr_plot_digits_classification_thumb.png)
[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) | ![](img/sphx_glr_plot_classification_probability_thumb.png)
[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) | ![](img/sphx_glr_plot_classifier_comparison_thumb.png)
[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) | | ![](img/sphx_glr_plot_lda_qda_thumb.png)
[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) | -## 集群 +## 多集群 有关[`sklearn.cluster`](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.cluster "sklearn.cluster")模块的示例。 | | | | | | -- | -- | -- | -- | -| ![](img/sphx_glr_plot_agglomerative_dendrogram_thumb.png)
[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) | ![](img/sphx_glr_plot_digits_agglomeration_thumb.png)
[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) | ![](img/sphx_glr_plot_mean_shift_thumb.png)
[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) | ![](img/sphx_glr_plot_kmeans_assumptions_thumb.png)
[的k均值假设示范](https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_assumptions.html#sphx-glr-auto-examples-cluster-plot-kmeans-assumptions-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id27) | -| ![](img/sphx_glr_plot_dict_face_patches_thumb.png)
[在线学习面部表情字典](https://scikit-learn.org/stable/auto_examples/cluster/plot_dict_face_patches.html#sphx-glr-auto-examples-cluster-plot-dict-face-patches-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id28) | ![](img/sphx_glr_plot_face_compress_thumb.png)
[矢量量化示例](https://scikit-learn.org/stable/auto_examples/cluster/plot_face_compress.html#sphx-glr-auto-examples-cluster-plot-face-compress-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id29) | ![](img/sphx_glr_plot_affinity_propagation_thumb.png)
[相似性传播聚类算法演示](https://scikit-learn.org/stable/auto_examples/cluster/plot_affinity_propagation.html#sphx-glr-auto-examples-cluster-plot-affinity-propagation-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id30) | ![](img/sphx_glr_plot_agglomerative_clustering_thumb.png)
[有和没有结构的聚集聚类](https://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_clustering.html#sphx-glr-auto-examples-cluster-plot-agglomerative-clustering-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id31) | -| ![](img/sphx_glr_plot_coin_segmentation_thumb.png)
[分割区域中希腊硬币的图片](https://scikit-learn.org/stable/auto_examples/cluster/plot_coin_segmentation.html#sphx-glr-auto-examples-cluster-plot-coin-segmentation-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id32) | ![](img/sphx_glr_plot_digits_linkage_thumb.png)
[二维数字嵌入中的各种聚集聚类](https://scikit-learn.org/stable/auto_examples/cluster/plot_digits_linkage.html#sphx-glr-auto-examples-cluster-plot-digits-linkage-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id33) | ![](img/sphx_glr_plot_cluster_iris_thumb.png)
[K-means聚类](https://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_iris.html#sphx-glr-auto-examples-cluster-plot-cluster-iris-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id34) | ![](img/sphx_glr_plot_segmentation_toy_thumb.png)
[光谱聚类用于图像分割](https://scikit-learn.org/stable/auto_examples/cluster/plot_segmentation_toy.html#sphx-glr-auto-examples-cluster-plot-segmentation-toy-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id35) | -| ![](img/sphx_glr_plot_coin_ward_segmentation_thumb.png)
[硬币图像上的结构化Ward层次聚类演示](https://scikit-learn.org/stable/auto_examples/cluster/plot_coin_ward_segmentation.html#sphx-glr-auto-examples-cluster-plot-coin-ward-segmentation-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id36) | ![](img/sphx_glr_plot_dbscan_thumb.png)
[DBSCAN聚类算法演示](https://scikit-learn.org/stable/auto_examples/cluster/plot_dbscan.html#sphx-glr-auto-examples-cluster-plot-dbscan-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id37) | ![](img/sphx_glr_plot_color_quantization_thumb.png)
[使用K均值的颜色量化](https://scikit-learn.org/stable/auto_examples/cluster/plot_color_quantization.html#sphx-glr-auto-examples-cluster-plot-color-quantization-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id38) | ![](img/sphx_glr_plot_ward_structured_vs_unstructured_thumb.png)
[分层聚类：结构化与非结构化病房](https://scikit-learn.org/stable/auto_examples/cluster/plot_ward_structured_vs_unstructured.html#sphx-glr-auto-examples-cluster-plot-ward-structured-vs-unstructured-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id39) | -| ![](img/sphx_glr_plot_agglomerative_clustering_metrics_thumb.png)
[具有不同指标的聚集集群](https://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_clustering_metrics.html#sphx-glr-auto-examples-cluster-plot-agglomerative-clustering-metrics-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id40) | ![](img/sphx_glr_plot_inductive_clustering_thumb.png)
[归纳聚类](https://scikit-learn.org/stable/auto_examples/cluster/plot_inductive_clustering.html#sphx-glr-auto-examples-cluster-plot-inductive-clustering-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id41) | ![](img/sphx_glr_plot_optics_thumb.png)
[OPTICS聚类算法演示](https://scikit-learn.org/stable/auto_examples/cluster/plot_optics.html#sphx-glr-auto-examples-cluster-plot-optics-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id42) | ![](img/sphx_glr_plot_birch_vs_minibatchkmeans_thumb.png)
[比较桦木和MiniBatchKMeans](https://scikit-learn.org/stable/auto_examples/cluster/plot_birch_vs_minibatchkmeans.html#sphx-glr-auto-examples-cluster-plot-birch-vs-minibatchkmeans-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id43) | -| ![](img/sphx_glr_plot_kmeans_stability_low_dim_dense_thumb.png)
[k均值初始化影响的实证评估](https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_stability_low_dim_dense.html#sphx-glr-auto-examples-cluster-plot-kmeans-stability-low-dim-dense-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id44) | ![](img/sphx_glr_plot_adjusted_for_chance_measures_thumb.png)
[集群绩效评估中机会的调整](https://scikit-learn.org/stable/auto_examples/cluster/plot_adjusted_for_chance_measures.html#sphx-glr-auto-examples-cluster-plot-adjusted-for-chance-measures-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id45) | ![](img/sphx_glr_plot_mini_batch_kmeans_thumb.png)
[K-Means和MiniBatchKMeans聚类算法的比较](https://scikit-learn.org/stable/auto_examples/cluster/plot_mini_batch_kmeans.html#sphx-glr-auto-examples-cluster-plot-mini-batch-kmeans-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id46) | ![](img/sphx_glr_plot_feature_agglomeration_vs_univariate_selection_thumb.png)
[特征集聚与单变量选择](https://scikit-learn.org/stable/auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection.html#sphx-glr-auto-examples-cluster-plot-feature-agglomeration-vs-univariate-selection-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id47) | +| ![](img/sphx_glr_plot_agglomerative_dendrogram_thumb.png)
[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) | ![](img/sphx_glr_plot_digits_agglomeration_thumb.png)
[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) | ![](img/sphx_glr_plot_mean_shift_thumb.png)
[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) | ![](img/sphx_glr_plot_kmeans_assumptions_thumb.png)
[的k均值假设示范](https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_assumptions.html#sphx-glr-auto-examples-cluster-plot-kmeans-assumptions-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id27) | +| ![](img/sphx_glr_plot_dict_face_patches_thumb.png)
[在线学习面部表情字典](https://scikit-learn.org/stable/auto_examples/cluster/plot_dict_face_patches.html#sphx-glr-auto-examples-cluster-plot-dict-face-patches-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id28) | ![](img/sphx_glr_plot_face_compress_thumb.png)
[矢量量化示例](https://scikit-learn.org/stable/auto_examples/cluster/plot_face_compress.html#sphx-glr-auto-examples-cluster-plot-face-compress-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id29) | ![](img/sphx_glr_plot_affinity_propagation_thumb.png)
[相似性传播聚类算法演示](https://scikit-learn.org/stable/auto_examples/cluster/plot_affinity_propagation.html#sphx-glr-auto-examples-cluster-plot-affinity-propagation-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id30) | ![](img/sphx_glr_plot_agglomerative_clustering_thumb.png)
[有和没有结构的聚集聚类](https://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_clustering.html#sphx-glr-auto-examples-cluster-plot-agglomerative-clustering-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id31) | +| ![](img/sphx_glr_plot_coin_segmentation_thumb.png)
[分割区域中希腊硬币的图片](https://scikit-learn.org/stable/auto_examples/cluster/plot_coin_segmentation.html#sphx-glr-auto-examples-cluster-plot-coin-segmentation-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id32) | ![](img/sphx_glr_plot_digits_linkage_thumb.png)
[二维数字嵌入中的各种聚集聚类](https://scikit-learn.org/stable/auto_examples/cluster/plot_digits_linkage.html#sphx-glr-auto-examples-cluster-plot-digits-linkage-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id33) | ![](img/sphx_glr_plot_cluster_iris_thumb.png)
[K-means聚类](https://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_iris.html#sphx-glr-auto-examples-cluster-plot-cluster-iris-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id34) | ![](img/sphx_glr_plot_segmentation_toy_thumb.png)
[光谱聚类用于图像分割](https://scikit-learn.org/stable/auto_examples/cluster/plot_segmentation_toy.html#sphx-glr-auto-examples-cluster-plot-segmentation-toy-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id35) | +| ![](img/sphx_glr_plot_coin_ward_segmentation_thumb.png)
[硬币图像上的结构化Ward层次聚类演示](https://scikit-learn.org/stable/auto_examples/cluster/plot_coin_ward_segmentation.html#sphx-glr-auto-examples-cluster-plot-coin-ward-segmentation-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id36) | ![](img/sphx_glr_plot_dbscan_thumb.png)
[DBSCAN聚类算法演示](https://scikit-learn.org/stable/auto_examples/cluster/plot_dbscan.html#sphx-glr-auto-examples-cluster-plot-dbscan-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id37) | ![](img/sphx_glr_plot_color_quantization_thumb.png)
[使用K均值的颜色量化](https://scikit-learn.org/stable/auto_examples/cluster/plot_color_quantization.html#sphx-glr-auto-examples-cluster-plot-color-quantization-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id38) | ![](img/sphx_glr_plot_ward_structured_vs_unstructured_thumb.png)
[分层聚类：结构化与非结构化病房](https://scikit-learn.org/stable/auto_examples/cluster/plot_ward_structured_vs_unstructured.html#sphx-glr-auto-examples-cluster-plot-ward-structured-vs-unstructured-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id39) | +| ![](img/sphx_glr_plot_agglomerative_clustering_metrics_thumb.png)
[具有不同指标的聚集集群](https://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_clustering_metrics.html#sphx-glr-auto-examples-cluster-plot-agglomerative-clustering-metrics-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id40) | ![](img/sphx_glr_plot_inductive_clustering_thumb.png)
[归纳聚类](https://scikit-learn.org/stable/auto_examples/cluster/plot_inductive_clustering.html#sphx-glr-auto-examples-cluster-plot-inductive-clustering-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id41) | ![](img/sphx_glr_plot_optics_thumb.png)
[OPTICS聚类算法演示](https://scikit-learn.org/stable/auto_examples/cluster/plot_optics.html#sphx-glr-auto-examples-cluster-plot-optics-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id42) | ![](img/sphx_glr_plot_birch_vs_minibatchkmeans_thumb.png)
[比较桦木和MiniBatchKMeans](https://scikit-learn.org/stable/auto_examples/cluster/plot_birch_vs_minibatchkmeans.html#sphx-glr-auto-examples-cluster-plot-birch-vs-minibatchkmeans-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id43) | +| ![](img/sphx_glr_plot_kmeans_stability_low_dim_dense_thumb.png)
[k均值初始化影响的实证评估](https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_stability_low_dim_dense.html#sphx-glr-auto-examples-cluster-plot-kmeans-stability-low-dim-dense-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id44) | ![](img/sphx_glr_plot_adjusted_for_chance_measures_thumb.png)
[集群绩效评估中机会的调整](https://scikit-learn.org/stable/auto_examples/cluster/plot_adjusted_for_chance_measures.html#sphx-glr-auto-examples-cluster-plot-adjusted-for-chance-measures-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id45) | ![](img/sphx_glr_plot_mini_batch_kmeans_thumb.png)
[K-Means和MiniBatchKMeans聚类算法的比较](https://scikit-learn.org/stable/auto_examples/cluster/plot_mini_batch_kmeans.html#sphx-glr-auto-examples-cluster-plot-mini-batch-kmeans-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id46) | ![](img/sphx_glr_plot_feature_agglomeration_vs_univariate_selection_thumb.png)
[特征集聚与单变量选择](https://scikit-learn.org/stable/auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection.html#sphx-glr-auto-examples-cluster-plot-feature-agglomeration-vs-univariate-selection-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id47) | | ![](img/sphx_glr_plot_kmeans_digits_thumb.png)
[手写数字数据上的K-Means聚类演示](https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_digits.html#sphx-glr-auto-examples-cluster-plot-kmeans-digits-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id48) | ![](img/sphx_glr_plot_linkage_comparison_thumb.png)
[比较玩具数据集上的不同层次链接方法](https://scikit-learn.org/stable/auto_examples/cluster/plot_linkage_comparison.html#sphx-glr-auto-examples-cluster-plot-linkage-comparison-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id49) | ![](img/sphx_glr_plot_kmeans_silhouette_analysis_thumb.png)
[在KMeans聚类上通过轮廓分析选择聚类数量](https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html#sphx-glr-auto-examples-cluster-plot-kmeans-silhouette-analysis-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id50) | ![](img/sphx_glr_plot_cluster_comparison_thumb.png)
[比较玩具数据集上的不同聚类算法](https://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html#sphx-glr-auto-examples-cluster-plot-cluster-comparison-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id51) | @@ -62,7 +62,7 @@ scikit-learn 的 Miscellaneous 和入门示例。 | | | | | | -- | -- | -- | -- | -| ![](img/sphx_glr_plot_lw_vs_oas_thumb.png)
[Ledoit-Wolf与OAS估计](https://scikit-learn.org/stable/auto_examples/covariance/plot_lw_vs_oas.html#sphx-glr-auto-examples-covariance-plot-lw-vs-oas-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id52) | ![](img/sphx_glr_plot_sparse_cov_thumb.png)
[稀疏逆协方差估计](https://scikit-learn.org/stable/auto_examples/covariance/plot_sparse_cov.html#sphx-glr-auto-examples-covariance-plot-sparse-cov-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id53) | ![](img/sphx_glr_plot_covariance_estimation_thumb.png)
[收缩协方差估计：LedoitWolf与OAS和最大似然性](https://scikit-learn.org/stable/auto_examples/covariance/plot_covariance_estimation.html#sphx-glr-auto-examples-covariance-plot-covariance-estimation-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id54) | ![](img/sphx_glr_plot_mahalanobis_distances_thumb.png)
[健壮的协方差估计和马氏距离相关性](https://scikit-learn.org/stable/auto_examples/covariance/plot_mahalanobis_distances.html#sphx-glr-auto-examples-covariance-plot-mahalanobis-distances-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id55) | +| ![](img/sphx_glr_plot_lw_vs_oas_thumb.png)
[Ledoit-Wolf与OAS估计](https://scikit-learn.org/stable/auto_examples/covariance/plot_lw_vs_oas.html#sphx-glr-auto-examples-covariance-plot-lw-vs-oas-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id52) | ![](img/sphx_glr_plot_sparse_cov_thumb.png)
[稀疏逆协方差估计](https://scikit-learn.org/stable/auto_examples/covariance/plot_sparse_cov.html#sphx-glr-auto-examples-covariance-plot-sparse-cov-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id53) | ![](img/sphx_glr_plot_covariance_estimation_thumb.png)
[收缩协方差估计：LedoitWolf与OAS和最大似然性](https://scikit-learn.org/stable/auto_examples/covariance/plot_covariance_estimation.html#sphx-glr-auto-examples-covariance-plot-covariance-estimation-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id54) | ![](img/sphx_glr_plot_mahalanobis_distances_thumb.png)
[健壮的协方差估计和马氏距离相关性](https://scikit-learn.org/stable/auto_examples/covariance/plot_mahalanobis_distances.html#sphx-glr-auto-examples-covariance-plot-mahalanobis-distances-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id55) | | ![](img/sphx_glr_plot_robust_vs_empirical_covariance_thumb.png)
[乐百氏VS实证协方差估计](https://scikit-learn.org/stable/auto_examples/covariance/plot_robust_vs_empirical_covariance.html#sphx-glr-auto-examples-covariance-plot-robust-vs-empirical-covariance-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id56) | @@ -75,7 +75,7 @@ scikit-learn 的 Miscellaneous 和入门示例。 | ![](img/sphx_glr_plot_compare_cross_decomposition_thumb.png)
[比较交叉分解方法](https://scikit-learn.org/stable/auto_examples/cross_decomposition/plot_compare_cross_decomposition.html#sphx-glr-auto-examples-cross-decomposition-plot-compare-cross-decomposition-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id57) | -## 数据集的示例 +## 数据集示例 有关[`sklearn.datasets`](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.datasets "sklearn.datasets")模块的示例。 @@ -90,7 +90,7 @@ scikit-learn 的 Miscellaneous 和入门示例。 | | | | | | -- | -- | -- | -- | -| ![](img/sphx_glr_plot_tree_regression_thumb.png)
[决策树回归](https://scikit-learn.org/stable/auto_examples/tree/plot_tree_regression.html#sphx-glr-auto-examples-tree-plot-tree-regression-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id62) | ![](img/sphx_glr_plot_tree_regression_multioutput_thumb.png)
[多路输出决策树回归](https://scikit-learn.org/stable/auto_examples/tree/plot_tree_regression_multioutput.html#sphx-glr-auto-examples-tree-plot-tree-regression-multioutput-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id63) | ![](img/sphx_glr_plot_iris_dtc_thumb.png)
[在虹膜数据集上绘制决策树的决策面](https://scikit-learn.org/stable/auto_examples/tree/plot_iris_dtc.html#sphx-glr-auto-examples-tree-plot-iris-dtc-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id64) | ![](img/sphx_glr_plot_cost_complexity_pruning_thumb.png)
[使用成本复杂度修剪来修剪修剪决策树](https://scikit-learn.org/stable/auto_examples/tree/plot_cost_complexity_pruning.html#sphx-glr-auto-examples-tree-plot-cost-complexity-pruning-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id65) | +| ![](img/sphx_glr_plot_tree_regression_thumb.png)
[决策树回归](https://scikit-learn.org/stable/auto_examples/tree/plot_tree_regression.html#sphx-glr-auto-examples-tree-plot-tree-regression-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id62) | ![](img/sphx_glr_plot_tree_regression_multioutput_thumb.png)
[多路输出决策树回归](https://scikit-learn.org/stable/auto_examples/tree/plot_tree_regression_multioutput.html#sphx-glr-auto-examples-tree-plot-tree-regression-multioutput-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id63) | ![](img/sphx_glr_plot_iris_dtc_thumb.png)
[在虹膜数据集上绘制决策树的决策面](https://scikit-learn.org/stable/auto_examples/tree/plot_iris_dtc.html#sphx-glr-auto-examples-tree-plot-iris-dtc-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id64) | ![](img/sphx_glr_plot_cost_complexity_pruning_thumb.png)
[使用成本复杂度修剪来修剪修剪决策树](https://scikit-learn.org/stable/auto_examples/tree/plot_cost_complexity_pruning.html#sphx-glr-auto-examples-tree-plot-cost-complexity-pruning-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id65) | | ![](img/sphx_glr_plot_unveil_tree_structure_thumb.png)
[了解决策树结构](https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html#sphx-glr-auto-examples-tree-plot-unveil-tree-structure-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id66) | @@ -100,8 +100,8 @@ scikit-learn 的 Miscellaneous 和入门示例。 | | | | | | -- | -- | -- | -- | -| ![](img/sphx_glr_plot_beta_divergence_thumb.png)
[β-发散损失函数](https://scikit-learn.org/stable/auto_examples/decomposition/plot_beta_divergence.html#sphx-glr-auto-examples-decomposition-plot-beta-divergence-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id67) | ![](img/sphx_glr_plot_pca_iris_thumb.png)
[具有虹膜数据集的PCA示例](https://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_iris.html#sphx-glr-auto-examples-decomposition-plot-pca-iris-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id68) | ![](img/sphx_glr_plot_incremental_pca_thumb.png)
[增量](https://scikit-learn.org/stable/auto_examples/decomposition/plot_incremental_pca.html#sphx-glr-auto-examples-decomposition-plot-incremental-pca-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id69) | ![](img/sphx_glr_plot_pca_vs_lda_thumb.png)
[Iris数据集的LDA和PCA二维投影的比较](https://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_lda.html#sphx-glr-auto-examples-decomposition-plot-pca-vs-lda-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id70) | -| ![](img/sphx_glr_plot_ica_blind_source_separation_thumb.png)
[使用FastICA进行盲源分离](https://scikit-learn.org/stable/auto_examples/decomposition/plot_ica_blind_source_separation.html#sphx-glr-auto-examples-decomposition-plot-ica-blind-source-separation-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id71) | ![](img/sphx_glr_plot_pca_3d_thumb.png)
[主成分分析（PCA）](https://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_3d.html#sphx-glr-auto-examples-decomposition-plot-pca-3d-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id72) | ![](img/sphx_glr_plot_ica_vs_pca_thumb.png)
[2D点云上的](https://scikit-learn.org/stable/auto_examples/decomposition/plot_ica_vs_pca.html#sphx-glr-auto-examples-decomposition-plot-ica-vs-pca-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id73) | ![](img/sphx_glr_plot_kernel_pca_thumb.png)
[内核](https://scikit-learn.org/stable/auto_examples/decomposition/plot_kernel_pca.html#sphx-glr-auto-examples-decomposition-plot-kernel-pca-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id74) | +| ![](img/sphx_glr_plot_beta_divergence_thumb.png)
[β-发散损失函数](https://scikit-learn.org/stable/auto_examples/decomposition/plot_beta_divergence.html#sphx-glr-auto-examples-decomposition-plot-beta-divergence-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id67) | ![](img/sphx_glr_plot_pca_iris_thumb.png)
[具有虹膜数据集的PCA示例](https://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_iris.html#sphx-glr-auto-examples-decomposition-plot-pca-iris-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id68) | ![](img/sphx_glr_plot_incremental_pca_thumb.png)
[增量](https://scikit-learn.org/stable/auto_examples/decomposition/plot_incremental_pca.html#sphx-glr-auto-examples-decomposition-plot-incremental-pca-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id69) | ![](img/sphx_glr_plot_pca_vs_lda_thumb.png)
[Iris数据集的LDA和PCA二维投影的比较](https://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_lda.html#sphx-glr-auto-examples-decomposition-plot-pca-vs-lda-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id70) | +| ![](img/sphx_glr_plot_ica_blind_source_separation_thumb.png)
[使用FastICA进行盲源分离](https://scikit-learn.org/stable/auto_examples/decomposition/plot_ica_blind_source_separation.html#sphx-glr-auto-examples-decomposition-plot-ica-blind-source-separation-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id71) | ![](img/sphx_glr_plot_pca_3d_thumb.png)
[主成分分析（PCA）](https://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_3d.html#sphx-glr-auto-examples-decomposition-plot-pca-3d-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id72) | ![](img/sphx_glr_plot_ica_vs_pca_thumb.png)
[2D点云上的](https://scikit-learn.org/stable/auto_examples/decomposition/plot_ica_vs_pca.html#sphx-glr-auto-examples-decomposition-plot-ica-vs-pca-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id73) | ![](img/sphx_glr_plot_kernel_pca_thumb.png)
[内核](https://scikit-learn.org/stable/auto_examples/decomposition/plot_kernel_pca.html#sphx-glr-auto-examples-decomposition-plot-kernel-pca-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id74) | | ![](img/sphx_glr_plot_pca_vs_fa_model_selection_thumb.png)
[概率PCA和因子分析（FA）进行模型选择](https://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_fa_model_selection.html#sphx-glr-auto-examples-decomposition-plot-pca-vs-fa-model-selection-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id75) | ![](img/sphx_glr_plot_sparse_coding_thumb.png)
[使用预先计算的字典进行稀疏编码](https://scikit-learn.org/stable/auto_examples/decomposition/plot_sparse_coding.html#sphx-glr-auto-examples-decomposition-plot-sparse-coding-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id76) | ![](img/sphx_glr_plot_image_denoising_thumb.png)
[图片使用字典学习去噪](https://scikit-learn.org/stable/auto_examples/decomposition/plot_image_denoising.html#sphx-glr-auto-examples-decomposition-plot-image-denoising-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id77) | ![](img/sphx_glr_plot_faces_decomposition_thumb.png)
[Faces数据集分解](https://scikit-learn.org/stable/auto_examples/decomposition/plot_faces_decomposition.html#sphx-glr-auto-examples-decomposition-plot-faces-decomposition-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id78) | @@ -111,22 +111,22 @@ scikit-learn 的 Miscellaneous 和入门示例。 | | | | | | -- | -- | -- | -- | -| ![](img/sphx_glr_plot_forest_importances_faces_thumb.png)
[并行树木森林的像素重要性](https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances_faces.html#sphx-glr-auto-examples-ensemble-plot-forest-importances-faces-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id79) | ![](img/sphx_glr_plot_adaboost_regression_thumb.png)
[使用AdaBoost进行决策树回归](https://scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_regression.html#sphx-glr-auto-examples-ensemble-plot-adaboost-regression-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id80) | ![](img/sphx_glr_plot_voting_regressor_thumb.png)
[绘制个人和投票回归预测](https://scikit-learn.org/stable/auto_examples/ensemble/plot_voting_regressor.html#sphx-glr-auto-examples-ensemble-plot-voting-regressor-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id81) | ![](img/sphx_glr_plot_forest_importances_thumb.png)
[树木森林的功能重要性](https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html#sphx-glr-auto-examples-ensemble-plot-forest-importances-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id82) | -| ![](img/sphx_glr_plot_isolation_forest_thumb.png)
[IsolationForest示例](https://scikit-learn.org/stable/auto_examples/ensemble/plot_isolation_forest.html#sphx-glr-auto-examples-ensemble-plot-isolation-forest-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id83) | ![](img/sphx_glr_plot_voting_decision_regions_thumb.png)
[绘制VotingClassifier的决策边界](https://scikit-learn.org/stable/auto_examples/ensemble/plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id84) | ![](img/sphx_glr_plot_random_forest_regression_multioutput_thumb.png)
[比较随机森林和多输出元估计器](https://scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_regression_multioutput.html#sphx-glr-auto-examples-ensemble-plot-random-forest-regression-multioutput-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id85) | ![](img/sphx_glr_plot_gradient_boosting_quantile_thumb.png)
[梯度提升回归的预测间隔](https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_quantile.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-quantile-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id86) | -| ![](img/sphx_glr_plot_gradient_boosting_regularization_thumb.png)
[梯度提升正则化](https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regularization.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-regularization-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id87) | ![](img/sphx_glr_plot_voting_probas_thumb.png)
[绘制由VotingClassifier计算的类概率](https://scikit-learn.org/stable/auto_examples/ensemble/plot_voting_probas.html#sphx-glr-auto-examples-ensemble-plot-voting-probas-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id88) | ![](img/sphx_glr_plot_gradient_boosting_regression_thumb.png)
[梯度推进回归](https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regression.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-regression-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id89) | ![](img/sphx_glr_plot_ensemble_oob_thumb.png)
[随机森林的OOB错误](https://scikit-learn.org/stable/auto_examples/ensemble/plot_ensemble_oob.html#sphx-glr-auto-examples-ensemble-plot-ensemble-oob-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id90) | -| ![](img/sphx_glr_plot_adaboost_twoclass_thumb.png)
[两个级的AdaBoost](https://scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_twoclass.html#sphx-glr-auto-examples-ensemble-plot-adaboost-twoclass-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id91) | ![](img/sphx_glr_plot_random_forest_embedding_thumb.png)
[使用完全随机树的哈希特征转换](https://scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_embedding.html#sphx-glr-auto-examples-ensemble-plot-random-forest-embedding-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id92) | ![](img/sphx_glr_plot_adaboost_multiclass_thumb.png)
[多类AdaBoosted决策树](https://scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_multiclass.html#sphx-glr-auto-examples-ensemble-plot-adaboost-multiclass-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id93) | ![](img/sphx_glr_plot_adaboost_hastie_10_2_thumb.png)
[离散相对真正的AdaBoost](https://scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_hastie_10_2.html#sphx-glr-auto-examples-ensemble-plot-adaboost-hastie-10-2-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id94) | -| ![](img/sphx_glr_plot_stack_predictors_thumb.png)
[使用堆叠组合预测](https://scikit-learn.org/stable/auto_examples/ensemble/plot_stack_predictors.html#sphx-glr-auto-examples-ensemble-plot-stack-predictors-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id95) | ![](img/sphx_glr_plot_gradient_boosting_early_stopping_thumb.png)
[提前终止的梯度推进](https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_early_stopping.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-early-stopping-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id96) | ![](img/sphx_glr_plot_feature_transformation_thumb.png)
[带有树群的特征变换](https://scikit-learn.org/stable/auto_examples/ensemble/plot_feature_transformation.html#sphx-glr-auto-examples-ensemble-plot-feature-transformation-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id97) | ![](img/sphx_glr_plot_gradient_boosting_oob_thumb.png)
[梯度提升袋外估计](https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_oob.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-oob-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id98) | +| ![](img/sphx_glr_plot_forest_importances_faces_thumb.png)
[并行树木森林的像素重要性](https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances_faces.html#sphx-glr-auto-examples-ensemble-plot-forest-importances-faces-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id79) | ![](img/sphx_glr_plot_adaboost_regression_thumb.png)
[使用AdaBoost进行决策树回归](https://scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_regression.html#sphx-glr-auto-examples-ensemble-plot-adaboost-regression-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id80) | ![](img/sphx_glr_plot_voting_regressor_thumb.png)
[绘制个人和投票回归预测](https://scikit-learn.org/stable/auto_examples/ensemble/plot_voting_regressor.html#sphx-glr-auto-examples-ensemble-plot-voting-regressor-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id81) | ![](img/sphx_glr_plot_forest_importances_thumb.png)
[树木森林的功能重要性](https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html#sphx-glr-auto-examples-ensemble-plot-forest-importances-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id82) | +| ![](img/sphx_glr_plot_isolation_forest_thumb.png)
[IsolationForest示例](https://scikit-learn.org/stable/auto_examples/ensemble/plot_isolation_forest.html#sphx-glr-auto-examples-ensemble-plot-isolation-forest-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id83) | ![](img/sphx_glr_plot_voting_decision_regions_thumb.png)
[绘制VotingClassifier的决策边界](https://scikit-learn.org/stable/auto_examples/ensemble/plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id84) | ![](img/sphx_glr_plot_random_forest_regression_multioutput_thumb.png)
[比较随机森林和多输出元估计器](https://scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_regression_multioutput.html#sphx-glr-auto-examples-ensemble-plot-random-forest-regression-multioutput-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id85) | ![](img/sphx_glr_plot_gradient_boosting_quantile_thumb.png)
[梯度提升回归的预测间隔](https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_quantile.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-quantile-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id86) | +| ![](img/sphx_glr_plot_gradient_boosting_regularization_thumb.png)
[梯度提升正则化](https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regularization.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-regularization-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id87) | ![](img/sphx_glr_plot_voting_probas_thumb.png)
[绘制由VotingClassifier计算的类概率](https://scikit-learn.org/stable/auto_examples/ensemble/plot_voting_probas.html#sphx-glr-auto-examples-ensemble-plot-voting-probas-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id88) | ![](img/sphx_glr_plot_gradient_boosting_regression_thumb.png)
[梯度推进回归](https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regression.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-regression-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id89) | ![](img/sphx_glr_plot_ensemble_oob_thumb.png)
[随机森林的OOB错误](https://scikit-learn.org/stable/auto_examples/ensemble/plot_ensemble_oob.html#sphx-glr-auto-examples-ensemble-plot-ensemble-oob-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id90) | +| ![](img/sphx_glr_plot_adaboost_twoclass_thumb.png)
[两个级的AdaBoost](https://scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_twoclass.html#sphx-glr-auto-examples-ensemble-plot-adaboost-twoclass-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id91) | ![](img/sphx_glr_plot_random_forest_embedding_thumb.png)
[使用完全随机树的哈希特征转换](https://scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_embedding.html#sphx-glr-auto-examples-ensemble-plot-random-forest-embedding-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id92) | ![](img/sphx_glr_plot_adaboost_multiclass_thumb.png)
[多类AdaBoosted决策树](https://scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_multiclass.html#sphx-glr-auto-examples-ensemble-plot-adaboost-multiclass-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id93) | ![](img/sphx_glr_plot_adaboost_hastie_10_2_thumb.png)
[离散相对真正的AdaBoost](https://scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_hastie_10_2.html#sphx-glr-auto-examples-ensemble-plot-adaboost-hastie-10-2-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id94) | +| ![](img/sphx_glr_plot_stack_predictors_thumb.png)
[使用堆叠组合预测](https://scikit-learn.org/stable/auto_examples/ensemble/plot_stack_predictors.html#sphx-glr-auto-examples-ensemble-plot-stack-predictors-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id95) | ![](img/sphx_glr_plot_gradient_boosting_early_stopping_thumb.png)
[提前终止的梯度推进](https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_early_stopping.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-early-stopping-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id96) | ![](img/sphx_glr_plot_feature_transformation_thumb.png)
[带有树群的特征变换](https://scikit-learn.org/stable/auto_examples/ensemble/plot_feature_transformation.html#sphx-glr-auto-examples-ensemble-plot-feature-transformation-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id97) | ![](img/sphx_glr_plot_gradient_boosting_oob_thumb.png)
[梯度提升袋外估计](https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_oob.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-oob-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id98) | | ![](img/sphx_glr_plot_bias_variance_thumb.png)
[单一估计器与装袋：偏差方差分解](https://scikit-learn.org/stable/auto_examples/ensemble/plot_bias_variance.html#sphx-glr-auto-examples-ensemble-plot-bias-variance-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id99) | ![](img/sphx_glr_plot_forest_iris_thumb.png)
[在虹膜数据集上绘制树木合奏的决策面](https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_iris.html#sphx-glr-auto-examples-ensemble-plot-forest-iris-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id100) | -## 基于现实世界数据集的示例 +## 基于真实数据集的示例 具有一些中等大小的数据集或交互式用户界面的现实问题的应用程序。 | | | | | | -- | -- | -- | -- | -| ![](img/sphx_glr_plot_outlier_detection_housing_thumb.png)
[真实数据集的异常值检测](https://scikit-learn.org/stable/auto_examples/applications/plot_outlier_detection_housing.html#sphx-glr-auto-examples-applications-plot-outlier-detection-housing-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id101) | ![](img/sphx_glr_plot_tomography_l1_reconstruction_thumb.png)
[压缩感测：使用L1先验（Lasso）进行层析成像重建](https://scikit-learn.org/stable/auto_examples/applications/plot_tomography_l1_reconstruction.html#sphx-glr-auto-examples-applications-plot-tomography-l1-reconstruction-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id102) | ![](img/sphx_glr_plot_topics_extraction_with_nmf_lda_thumb.png)
[非负矩阵分解和隐含狄利克雷分布话题提取](https://scikit-learn.org/stable/auto_examples/applications/plot_topics_extraction_with_nmf_lda.html#sphx-glr-auto-examples-applications-plot-topics-extraction-with-nmf-lda-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id103) | ![](img/sphx_glr_plot_face_recognition_thumb.png)
[使用特征脸和支持向量机的](https://scikit-learn.org/stable/auto_examples/applications/plot_face_recognition.html#sphx-glr-auto-examples-applications-plot-face-recognition-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id104 "Permalink to this image")[识别示例](https://scikit-learn.org/stable/auto_examples/applications/plot_face_recognition.html#sphx-glr-auto-examples-applications-plot-face-recognition-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id104) | -| ![](img/sphx_glr_plot_model_complexity_influence_thumb.png)
[模型复杂度影响](https://scikit-learn.org/stable/auto_examples/applications/plot_model_complexity_influence.html#sphx-glr-auto-examples-applications-plot-model-complexity-influence-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id105) | ![](img/sphx_glr_plot_stock_market_thumb.png)
[可视化的股市结构](https://scikit-learn.org/stable/auto_examples/applications/plot_stock_market.html#sphx-glr-auto-examples-applications-plot-stock-market-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id106) | ![](img/sphx_glr_wikipedia_principal_eigenvector_thumb.png)
[维基百科的主要特征向量](https://scikit-learn.org/stable/auto_examples/applications/wikipedia_principal_eigenvector.html#sphx-glr-auto-examples-applications-wikipedia-principal-eigenvector-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id107) | ![](img/sphx_glr_plot_species_distribution_modeling_thumb.png)
[物种分布建模](https://scikit-learn.org/stable/auto_examples/applications/plot_species_distribution_modeling.html#sphx-glr-auto-examples-applications-plot-species-distribution-modeling-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id108) | +| ![](img/sphx_glr_plot_outlier_detection_housing_thumb.png)
[真实数据集的异常值检测](https://scikit-learn.org/stable/auto_examples/applications/plot_outlier_detection_housing.html#sphx-glr-auto-examples-applications-plot-outlier-detection-housing-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id101) | ![](img/sphx_glr_plot_tomography_l1_reconstruction_thumb.png)
[压缩感测：使用L1先验（Lasso）进行层析成像重建](https://scikit-learn.org/stable/auto_examples/applications/plot_tomography_l1_reconstruction.html#sphx-glr-auto-examples-applications-plot-tomography-l1-reconstruction-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id102) | ![](img/sphx_glr_plot_topics_extraction_with_nmf_lda_thumb.png)
[非负矩阵分解和隐含狄利克雷分布话题提取](https://scikit-learn.org/stable/auto_examples/applications/plot_topics_extraction_with_nmf_lda.html#sphx-glr-auto-examples-applications-plot-topics-extraction-with-nmf-lda-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id103) | ![](img/sphx_glr_plot_face_recognition_thumb.png)
[使用特征脸和支持向量机的](https://scikit-learn.org/stable/auto_examples/applications/plot_face_recognition.html#sphx-glr-auto-examples-applications-plot-face-recognition-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id104 "Permalink to this image")[识别示例](https://scikit-learn.org/stable/auto_examples/applications/plot_face_recognition.html#sphx-glr-auto-examples-applications-plot-face-recognition-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id104) | +| ![](img/sphx_glr_plot_model_complexity_influence_thumb.png)
[模型复杂度影响](https://scikit-learn.org/stable/auto_examples/applications/plot_model_complexity_influence.html#sphx-glr-auto-examples-applications-plot-model-complexity-influence-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id105) | ![](img/sphx_glr_plot_stock_market_thumb.png)
[可视化的股市结构](https://scikit-learn.org/stable/auto_examples/applications/plot_stock_market.html#sphx-glr-auto-examples-applications-plot-stock-market-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id106) | ![](img/sphx_glr_wikipedia_principal_eigenvector_thumb.png)
[维基百科的主要特征向量](https://scikit-learn.org/stable/auto_examples/applications/wikipedia_principal_eigenvector.html#sphx-glr-auto-examples-applications-wikipedia-principal-eigenvector-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id107) | ![](img/sphx_glr_plot_species_distribution_modeling_thumb.png)
[物种分布建模](https://scikit-learn.org/stable/auto_examples/applications/plot_species_distribution_modeling.html#sphx-glr-auto-examples-applications-plot-species-distribution-modeling-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id108) | | ![](img/sphx_glr_svm_gui_thumb.png)
[Libsvm](https://scikit-learn.org/stable/auto_examples/applications/svm_gui.html#sphx-glr-auto-examples-applications-svm-gui-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id109) | ![](img/sphx_glr_plot_prediction_latency_thumb.png)
[预测延迟](https://scikit-learn.org/stable/auto_examples/applications/plot_prediction_latency.html#sphx-glr-auto-examples-applications-plot-prediction-latency-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id110) | ![](img/sphx_glr_plot_out_of_core_classification_thumb.png)
[文本文档的核心分类](https://scikit-learn.org/stable/auto_examples/applications/plot_out_of_core_classification.html#sphx-glr-auto-examples-applications-plot-out-of-core-classification-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id111) | @@ -136,7 +136,7 @@ scikit-learn 的 Miscellaneous 和入门示例。 | | | | | | -- | -- | -- | -- | -| ![](img/sphx_glr_plot_rfe_digits_thumb.png)
[递归特征消除](https://scikit-learn.org/stable/auto_examples/feature_selection/plot_rfe_digits.html#sphx-glr-auto-examples-feature-selection-plot-rfe-digits-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id112) | ![](img/sphx_glr_plot_f_test_vs_mi_thumb.png)
[F检验和相互信息的比较](https://scikit-learn.org/stable/auto_examples/feature_selection/plot_f_test_vs_mi.html#sphx-glr-auto-examples-feature-selection-plot-f-test-vs-mi-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id113) | ![](img/sphx_glr_plot_feature_selection_pipeline_thumb.png)
[管道Anova](https://scikit-learn.org/stable/auto_examples/feature_selection/plot_feature_selection_pipeline.html#sphx-glr-auto-examples-feature-selection-plot-feature-selection-pipeline-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id114) | ![](img/sphx_glr_plot_rfe_with_cross_validation_thumb.png)
[通过交叉验证消除递归特征](https://scikit-learn.org/stable/auto_examples/feature_selection/plot_rfe_with_cross_validation.html#sphx-glr-auto-examples-feature-selection-plot-rfe-with-cross-validation-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id115) | +| ![](img/sphx_glr_plot_rfe_digits_thumb.png)
[递归特征消除](https://scikit-learn.org/stable/auto_examples/feature_selection/plot_rfe_digits.html#sphx-glr-auto-examples-feature-selection-plot-rfe-digits-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id112) | ![](img/sphx_glr_plot_f_test_vs_mi_thumb.png)
[F检验和相互信息的比较](https://scikit-learn.org/stable/auto_examples/feature_selection/plot_f_test_vs_mi.html#sphx-glr-auto-examples-feature-selection-plot-f-test-vs-mi-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id113) | ![](img/sphx_glr_plot_feature_selection_pipeline_thumb.png)
[管道Anova](https://scikit-learn.org/stable/auto_examples/feature_selection/plot_feature_selection_pipeline.html#sphx-glr-auto-examples-feature-selection-plot-feature-selection-pipeline-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id114) | ![](img/sphx_glr_plot_rfe_with_cross_validation_thumb.png)
[通过交叉验证消除递归特征](https://scikit-learn.org/stable/auto_examples/feature_selection/plot_rfe_with_cross_validation.html#sphx-glr-auto-examples-feature-selection-plot-rfe-with-cross-validation-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id115) | | ![](img/sphx_glr_plot_select_from_model_boston_thumb.png)
[使用SelectFromModel和LassoCV特征选择](https://scikit-learn.org/stable/auto_examples/feature_selection/plot_select_from_model_boston.html#sphx-glr-auto-examples-feature-selection-plot-select-from-model-boston-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id116) | ![](img/sphx_glr_plot_permutation_test_for_classification_thumb.png)
[与排列测试的分类评分的意义](https://scikit-learn.org/stable/auto_examples/feature_selection/plot_permutation_test_for_classification.html#sphx-glr-auto-examples-feature-selection-plot-permutation-test-for-classification-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id117) | ![](img/sphx_glr_plot_feature_selection_thumb.png)
[单变量特征选择](https://scikit-learn.org/stable/auto_examples/feature_selection/plot_feature_selection.html#sphx-glr-auto-examples-feature-selection-plot-feature-selection-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id118) | @@ -146,7 +146,7 @@ scikit-learn 的 Miscellaneous 和入门示例。 | | | | | | -- | -- | -- | -- | -| ![](img/sphx_glr_plot_gmm_pdf_thumb.png)
[高斯混合的密度估计](https://scikit-learn.org/stable/auto_examples/mixture/plot_gmm_pdf.html#sphx-glr-auto-examples-mixture-plot-gmm-pdf-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id119) | ![](img/sphx_glr_plot_gmm_thumb.png)
[高斯混合模型椭球](https://scikit-learn.org/stable/auto_examples/mixture/plot_gmm.html#sphx-glr-auto-examples-mixture-plot-gmm-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id120) | ![](img/sphx_glr_plot_gmm_selection_thumb.png)
[高斯混合模型选择](https://scikit-learn.org/stable/auto_examples/mixture/plot_gmm_selection.html#sphx-glr-auto-examples-mixture-plot-gmm-selection-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id121) | ![](img/sphx_glr_plot_gmm_covariances_thumb.png)
[GMM协方差](https://scikit-learn.org/stable/auto_examples/mixture/plot_gmm_covariances.html#sphx-glr-auto-examples-mixture-plot-gmm-covariances-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id122) | +| ![](img/sphx_glr_plot_gmm_pdf_thumb.png)
[高斯混合的密度估计](https://scikit-learn.org/stable/auto_examples/mixture/plot_gmm_pdf.html#sphx-glr-auto-examples-mixture-plot-gmm-pdf-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id119) | ![](img/sphx_glr_plot_gmm_thumb.png)
[高斯混合模型椭球](https://scikit-learn.org/stable/auto_examples/mixture/plot_gmm.html#sphx-glr-auto-examples-mixture-plot-gmm-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id120) | ![](img/sphx_glr_plot_gmm_selection_thumb.png)
[高斯混合模型选择](https://scikit-learn.org/stable/auto_examples/mixture/plot_gmm_selection.html#sphx-glr-auto-examples-mixture-plot-gmm-selection-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id121) | ![](img/sphx_glr_plot_gmm_covariances_thumb.png)
[GMM协方差](https://scikit-learn.org/stable/auto_examples/mixture/plot_gmm_covariances.html#sphx-glr-auto-examples-mixture-plot-gmm-covariances-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id122) | | ![](img/sphx_glr_plot_gmm_sin_thumb.png)
[高斯混合模型正弦曲线](https://scikit-learn.org/stable/auto_examples/mixture/plot_gmm_sin.html#sphx-glr-auto-examples-mixture-plot-gmm-sin-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id123) | ![](img/sphx_glr_plot_concentration_prior_thumb.png)
[贝叶斯高斯混合变量的浓度先验类型分析](https://scikit-learn.org/stable/auto_examples/mixture/plot_concentration_prior.html#sphx-glr-auto-examples-mixture-plot-concentration-prior-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id124) | @@ -156,8 +156,8 @@ scikit-learn 的 Miscellaneous 和入门示例。 | | | | | | -- | -- | -- | -- | -| ![](img/sphx_glr_plot_gpc_xor_thumb.png)
[XOR数据集上的高斯过程分类（GPC）的图示](https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpc_xor.html#sphx-glr-auto-examples-gaussian-process-plot-gpc-xor-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id125) | ![](img/sphx_glr_plot_gpc_iris_thumb.png)
[虹膜数据集上的高斯过程分类（GPC）](https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpc_iris.html#sphx-glr-auto-examples-gaussian-process-plot-gpc-iris-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id126) | ![](img/sphx_glr_plot_compare_gpr_krr_thumb.png)
[核岭和高斯过程回归的比较](https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_compare_gpr_krr.html#sphx-glr-auto-examples-gaussian-process-plot-compare-gpr-krr-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id127) | ![](img/sphx_glr_plot_gpr_prior_posterior_thumb.png)
[不同内核的先验和后验高斯过程的图示](https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_prior_posterior.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-prior-posterior-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id128) | -| ![](img/sphx_glr_plot_gpc_isoprobability_thumb.png)
[高斯过程分类（GPC）的等概率线](https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpc_isoprobability.html#sphx-glr-auto-examples-gaussian-process-plot-gpc-isoprobability-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id129) | ![](img/sphx_glr_plot_gpc_thumb.png)
[概率预测的结果与高斯过程分类（GPC）](https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpc.html#sphx-glr-auto-examples-gaussian-process-plot-gpc-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id130) | ![](img/sphx_glr_plot_gpr_noisy_thumb.png)
[具有噪声水平估计的高斯过程回归（GPR）](https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_noisy.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-noisy-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id131) | ![](img/sphx_glr_plot_gpr_noisy_targets_thumb.png)
[高斯过程回归：基本入门示例](https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_noisy_targets.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-noisy-targets-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id132) | +| ![](img/sphx_glr_plot_gpc_xor_thumb.png)
[XOR数据集上的高斯过程分类（GPC）的图示](https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpc_xor.html#sphx-glr-auto-examples-gaussian-process-plot-gpc-xor-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id125) | ![](img/sphx_glr_plot_gpc_iris_thumb.png)
[虹膜数据集上的高斯过程分类（GPC）](https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpc_iris.html#sphx-glr-auto-examples-gaussian-process-plot-gpc-iris-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id126) | ![](img/sphx_glr_plot_compare_gpr_krr_thumb.png)
[核岭和高斯过程回归的比较](https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_compare_gpr_krr.html#sphx-glr-auto-examples-gaussian-process-plot-compare-gpr-krr-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id127) | ![](img/sphx_glr_plot_gpr_prior_posterior_thumb.png)
[不同内核的先验和后验高斯过程的图示](https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_prior_posterior.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-prior-posterior-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id128) | +| ![](img/sphx_glr_plot_gpc_isoprobability_thumb.png)
[高斯过程分类（GPC）的等概率线](https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpc_isoprobability.html#sphx-glr-auto-examples-gaussian-process-plot-gpc-isoprobability-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id129) | ![](img/sphx_glr_plot_gpc_thumb.png)
[概率预测的结果与高斯过程分类（GPC）](https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpc.html#sphx-glr-auto-examples-gaussian-process-plot-gpc-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id130) | ![](img/sphx_glr_plot_gpr_noisy_thumb.png)
[具有噪声水平估计的高斯过程回归（GPR）](https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_noisy.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-noisy-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id131) | ![](img/sphx_glr_plot_gpr_noisy_targets_thumb.png)
[高斯过程回归：基本入门示例](https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_noisy_targets.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-noisy-targets-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id132) | | ![](img/sphx_glr_plot_gpr_co2_thumb.png)
[基于Mauna Loa CO2数据的高斯过程回归（GPR）。](https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_co2.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-co2-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id133) | ![](img/sphx_glr_plot_gpr_on_structured_data_thumb.png)
[离散数据结构上的高斯过程](https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_on_structured_data.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-on-structured-data-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id134) | @@ -167,14 +167,14 @@ scikit-learn 的 Miscellaneous 和入门示例。 | | | | | | -- | -- | -- | -- | -| ![](img/sphx_glr_plot_lasso_lars_thumb.png)
[使用LARS的套索路径](https://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_lars.html#sphx-glr-auto-examples-linear-model-plot-lasso-lars-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id135) | ![](img/sphx_glr_plot_ridge_path_thumb.png)
[绘制岭系数作为正则化的函数](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ridge_path.html#sphx-glr-auto-examples-linear-model-plot-ridge-path-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id136) | ![](img/sphx_glr_plot_sgd_separating_hyperplane_thumb.png)
[SGD：最大余量分隔超平面](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_separating_hyperplane.html#sphx-glr-auto-examples-linear-model-plot-sgd-separating-hyperplane-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id137) | ![](img/sphx_glr_plot_sgd_loss_functions_thumb.png)
[SGD：凸损失函数](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_loss_functions.html#sphx-glr-auto-examples-linear-model-plot-sgd-loss-functions-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id138) | -| ![](img/sphx_glr_plot_ols_ridge_variance_thumb.png)
[普通最小二乘法和岭回归方差](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols_ridge_variance.html#sphx-glr-auto-examples-linear-model-plot-ols-ridge-variance-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id139) | ![](img/sphx_glr_plot_ridge_coeffs_thumb.png)
[绘制Ridge系数作为L2正则化的函数](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ridge_coeffs.html#sphx-glr-auto-examples-linear-model-plot-ridge-coeffs-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id140) | ![](img/sphx_glr_plot_sgd_penalties_thumb.png)
[SGD：罚款](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_penalties.html#sphx-glr-auto-examples-linear-model-plot-sgd-penalties-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id141) | ![](img/sphx_glr_plot_polynomial_interpolation_thumb.png)
[多项式插值](https://scikit-learn.org/stable/auto_examples/linear_model/plot_polynomial_interpolation.html#sphx-glr-auto-examples-linear-model-plot-polynomial-interpolation-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id142) | -| ![](img/sphx_glr_plot_logistic_thumb.png)
[物流功能](https://scikit-learn.org/stable/auto_examples/linear_model/plot_logistic.html#sphx-glr-auto-examples-linear-model-plot-logistic-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id143) | ![](img/sphx_glr_plot_logistic_path_thumb.png)
[L1-Logistic回归的正规化道路](https://scikit-learn.org/stable/auto_examples/linear_model/plot_logistic_path.html#sphx-glr-auto-examples-linear-model-plot-logistic-path-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id144) | ![](img/sphx_glr_plot_iris_logistic_thumb.png)
[Logistic回归3类分类器](https://scikit-learn.org/stable/auto_examples/linear_model/plot_iris_logistic.html#sphx-glr-auto-examples-linear-model-plot-iris-logistic-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id145) | ![](img/sphx_glr_plot_sgd_weighted_samples_thumb.png)
[SGD：加权样本](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_weighted_samples.html#sphx-glr-auto-examples-linear-model-plot-sgd-weighted-samples-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id146) | -| ![](img/sphx_glr_plot_ols_thumb.png)
[线性回归示例](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id147) | ![](img/sphx_glr_plot_ransac_thumb.png)
[使用RANSAC进行稳健的线性模型估计](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ransac.html#sphx-glr-auto-examples-linear-model-plot-ransac-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id148) | ![](img/sphx_glr_plot_ols_3d_thumb.png)
[稀疏实施例：装修仅设有1和2](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols_3d.html#sphx-glr-auto-examples-linear-model-plot-ols-3d-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id149) | ![](img/sphx_glr_plot_huber_vs_ridge_thumb.png)
[HuberRegressor VS岭集具有较强的异常](https://scikit-learn.org/stable/auto_examples/linear_model/plot_huber_vs_ridge.html#sphx-glr-auto-examples-linear-model-plot-huber-vs-ridge-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id150) | -| ![](img/sphx_glr_plot_lasso_dense_vs_sparse_data_thumb.png)
[套索上密集和稀疏数据](https://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_dense_vs_sparse_data.html#sphx-glr-auto-examples-linear-model-plot-lasso-dense-vs-sparse-data-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id151) | ![](img/sphx_glr_plot_sgd_comparison_thumb.png)
[比较各种在线求解器](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_comparison.html#sphx-glr-auto-examples-linear-model-plot-sgd-comparison-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id152) | ![](img/sphx_glr_plot_multi_task_lasso_support_thumb.png)
[多任务套索的联合特征选择](https://scikit-learn.org/stable/auto_examples/linear_model/plot_multi_task_lasso_support.html#sphx-glr-auto-examples-linear-model-plot-multi-task-lasso-support-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id153) | ![](img/sphx_glr_plot_sparse_logistic_regression_mnist_thumb.png)
[使用多项式逻辑+ L1的MNIST分类](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sparse_logistic_regression_mnist.html#sphx-glr-auto-examples-linear-model-plot-sparse-logistic-regression-mnist-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id154) | ![](img/sphx_glr_plot_sgd_iris_thumb.png)
[在虹膜数据集上绘制多类](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_iris.html#sphx-glr-auto-examples-linear-model-plot-sgd-iris-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id155) | -| ![](img/sphx_glr_plot_omp_thumb.png)
[正交匹配追踪](https://scikit-learn.org/stable/auto_examples/linear_model/plot_omp.html#sphx-glr-auto-examples-linear-model-plot-omp-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id156) | ![](img/sphx_glr_plot_lasso_and_elasticnet_thumb.png)
[套索和弹性网用于稀疏信号](https://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_and_elasticnet.html#sphx-glr-auto-examples-linear-model-plot-lasso-and-elasticnet-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id157) | ![](img/sphx_glr_plot_bayesian_ridge_curvefit_thumb.png)
[贝叶斯岭回归的曲线拟合](https://scikit-learn.org/stable/auto_examples/linear_model/plot_bayesian_ridge_curvefit.html#sphx-glr-auto-examples-linear-model-plot-bayesian-ridge-curvefit-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id158) | ![](img/sphx_glr_plot_theilsen_thumb.png)
[Theil-Sen回归](https://scikit-learn.org/stable/auto_examples/linear_model/plot_theilsen.html#sphx-glr-auto-examples-linear-model-plot-theilsen-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id159) | -| ![](img/sphx_glr_plot_logistic_multinomial_thumb.png)
[绘制多项式和一对一静态Logistic回归](https://scikit-learn.org/stable/auto_examples/linear_model/plot_logistic_multinomial.html#sphx-glr-auto-examples-linear-model-plot-logistic-multinomial-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id160) | ![](img/sphx_glr_plot_robust_fit_thumb.png)
[稳健的线性估计器拟合](https://scikit-learn.org/stable/auto_examples/linear_model/plot_robust_fit.html#sphx-glr-auto-examples-linear-model-plot-robust-fit-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id161) | ![](img/sphx_glr_plot_logistic_l1_l2_sparsity_thumb.png)
[Logistic回归中的L1惩罚和稀疏性](https://scikit-learn.org/stable/auto_examples/linear_model/plot_logistic_l1_l2_sparsity.html#sphx-glr-auto-examples-linear-model-plot-logistic-l1-l2-sparsity-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id162) | ![](img/sphx_glr_plot_lasso_coordinate_descent_path_thumb.png)
[套索和弹性网络](https://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_coordinate_descent_path.html#sphx-glr-auto-examples-linear-model-plot-lasso-coordinate-descent-path-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id163) | -| ![](img/sphx_glr_plot_ard_thumb.png)
[自动相关性确定回归（ARD）](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ard.html#sphx-glr-auto-examples-linear-model-plot-ard-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id164) | ![](img/sphx_glr_plot_bayesian_ridge_thumb.png)
[贝叶斯岭回归](https://scikit-learn.org/stable/auto_examples/linear_model/plot_bayesian_ridge.html#sphx-glr-auto-examples-linear-model-plot-bayesian-ridge-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id165) | ![](img/sphx_glr_plot_sparse_logistic_regression_20newsgroups_thumb.png)
[20newgroups上的多类稀疏逻辑回归](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sparse_logistic_regression_20newsgroups.html#sphx-glr-auto-examples-linear-model-plot-sparse-logistic-regression-20newsgroups-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id166) | ![](img/sphx_glr_plot_lasso_model_selection_thumb.png)
[套索模型选择：交叉验证/ AIC /](https://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_model_selection.html#sphx-glr-auto-examples-linear-model-plot-lasso-model-selection-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id167) | +| ![](img/sphx_glr_plot_lasso_lars_thumb.png)
[使用LARS的套索路径](https://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_lars.html#sphx-glr-auto-examples-linear-model-plot-lasso-lars-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id135) | ![](img/sphx_glr_plot_ridge_path_thumb.png)
[绘制岭系数作为正则化的函数](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ridge_path.html#sphx-glr-auto-examples-linear-model-plot-ridge-path-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id136) | ![](img/sphx_glr_plot_sgd_separating_hyperplane_thumb.png)
[SGD：最大余量分隔超平面](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_separating_hyperplane.html#sphx-glr-auto-examples-linear-model-plot-sgd-separating-hyperplane-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id137) | ![](img/sphx_glr_plot_sgd_loss_functions_thumb.png)
[SGD：凸损失函数](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_loss_functions.html#sphx-glr-auto-examples-linear-model-plot-sgd-loss-functions-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id138) | +| ![](img/sphx_glr_plot_ols_ridge_variance_thumb.png)
[普通最小二乘法和岭回归方差](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols_ridge_variance.html#sphx-glr-auto-examples-linear-model-plot-ols-ridge-variance-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id139) | ![](img/sphx_glr_plot_ridge_coeffs_thumb.png)
[绘制Ridge系数作为L2正则化的函数](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ridge_coeffs.html#sphx-glr-auto-examples-linear-model-plot-ridge-coeffs-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id140) | ![](img/sphx_glr_plot_sgd_penalties_thumb.png)
[SGD：罚款](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_penalties.html#sphx-glr-auto-examples-linear-model-plot-sgd-penalties-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id141) | ![](img/sphx_glr_plot_polynomial_interpolation_thumb.png)
[多项式插值](https://scikit-learn.org/stable/auto_examples/linear_model/plot_polynomial_interpolation.html#sphx-glr-auto-examples-linear-model-plot-polynomial-interpolation-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id142) | +| ![](img/sphx_glr_plot_logistic_thumb.png)
[物流功能](https://scikit-learn.org/stable/auto_examples/linear_model/plot_logistic.html#sphx-glr-auto-examples-linear-model-plot-logistic-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id143) | ![](img/sphx_glr_plot_logistic_path_thumb.png)
[L1-Logistic回归的正规化道路](https://scikit-learn.org/stable/auto_examples/linear_model/plot_logistic_path.html#sphx-glr-auto-examples-linear-model-plot-logistic-path-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id144) | ![](img/sphx_glr_plot_iris_logistic_thumb.png)
[Logistic回归3类分类器](https://scikit-learn.org/stable/auto_examples/linear_model/plot_iris_logistic.html#sphx-glr-auto-examples-linear-model-plot-iris-logistic-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id145) | ![](img/sphx_glr_plot_sgd_weighted_samples_thumb.png)
[SGD：加权样本](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_weighted_samples.html#sphx-glr-auto-examples-linear-model-plot-sgd-weighted-samples-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id146) | +| ![](img/sphx_glr_plot_ols_thumb.png)
[线性回归示例](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id147) | ![](img/sphx_glr_plot_ransac_thumb.png)
[使用RANSAC进行稳健的线性模型估计](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ransac.html#sphx-glr-auto-examples-linear-model-plot-ransac-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id148) | ![](img/sphx_glr_plot_ols_3d_thumb.png)
[稀疏实施例：装修仅设有1和2](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols_3d.html#sphx-glr-auto-examples-linear-model-plot-ols-3d-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id149) | ![](img/sphx_glr_plot_huber_vs_ridge_thumb.png)
[HuberRegressor VS岭集具有较强的异常](https://scikit-learn.org/stable/auto_examples/linear_model/plot_huber_vs_ridge.html#sphx-glr-auto-examples-linear-model-plot-huber-vs-ridge-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id150) | +| ![](img/sphx_glr_plot_lasso_dense_vs_sparse_data_thumb.png)
[套索上密集和稀疏数据](https://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_dense_vs_sparse_data.html#sphx-glr-auto-examples-linear-model-plot-lasso-dense-vs-sparse-data-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id151) | ![](img/sphx_glr_plot_sgd_comparison_thumb.png)
[比较各种在线求解器](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_comparison.html#sphx-glr-auto-examples-linear-model-plot-sgd-comparison-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id152) | ![](img/sphx_glr_plot_multi_task_lasso_support_thumb.png)
[多任务套索的联合特征选择](https://scikit-learn.org/stable/auto_examples/linear_model/plot_multi_task_lasso_support.html#sphx-glr-auto-examples-linear-model-plot-multi-task-lasso-support-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id153) | ![](img/sphx_glr_plot_sparse_logistic_regression_mnist_thumb.png)
[使用多项式逻辑+ L1的MNIST分类](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sparse_logistic_regression_mnist.html#sphx-glr-auto-examples-linear-model-plot-sparse-logistic-regression-mnist-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id154) | ![](img/sphx_glr_plot_sgd_iris_thumb.png)
[在虹膜数据集上绘制多类](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_iris.html#sphx-glr-auto-examples-linear-model-plot-sgd-iris-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id155) | +| ![](img/sphx_glr_plot_omp_thumb.png)
[正交匹配追踪](https://scikit-learn.org/stable/auto_examples/linear_model/plot_omp.html#sphx-glr-auto-examples-linear-model-plot-omp-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id156) | ![](img/sphx_glr_plot_lasso_and_elasticnet_thumb.png)
[套索和弹性网用于稀疏信号](https://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_and_elasticnet.html#sphx-glr-auto-examples-linear-model-plot-lasso-and-elasticnet-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id157) | ![](img/sphx_glr_plot_bayesian_ridge_curvefit_thumb.png)
[贝叶斯岭回归的曲线拟合](https://scikit-learn.org/stable/auto_examples/linear_model/plot_bayesian_ridge_curvefit.html#sphx-glr-auto-examples-linear-model-plot-bayesian-ridge-curvefit-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id158) | ![](img/sphx_glr_plot_theilsen_thumb.png)
[Theil-Sen回归](https://scikit-learn.org/stable/auto_examples/linear_model/plot_theilsen.html#sphx-glr-auto-examples-linear-model-plot-theilsen-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id159) | +| ![](img/sphx_glr_plot_logistic_multinomial_thumb.png)
[绘制多项式和一对一静态Logistic回归](https://scikit-learn.org/stable/auto_examples/linear_model/plot_logistic_multinomial.html#sphx-glr-auto-examples-linear-model-plot-logistic-multinomial-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id160) | ![](img/sphx_glr_plot_robust_fit_thumb.png)
[稳健的线性估计器拟合](https://scikit-learn.org/stable/auto_examples/linear_model/plot_robust_fit.html#sphx-glr-auto-examples-linear-model-plot-robust-fit-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id161) | ![](img/sphx_glr_plot_logistic_l1_l2_sparsity_thumb.png)
[Logistic回归中的L1惩罚和稀疏性](https://scikit-learn.org/stable/auto_examples/linear_model/plot_logistic_l1_l2_sparsity.html#sphx-glr-auto-examples-linear-model-plot-logistic-l1-l2-sparsity-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id162) | ![](img/sphx_glr_plot_lasso_coordinate_descent_path_thumb.png)
[套索和弹性网络](https://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_coordinate_descent_path.html#sphx-glr-auto-examples-linear-model-plot-lasso-coordinate-descent-path-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id163) | +| ![](img/sphx_glr_plot_ard_thumb.png)
[自动相关性确定回归（ARD）](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ard.html#sphx-glr-auto-examples-linear-model-plot-ard-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id164) | ![](img/sphx_glr_plot_bayesian_ridge_thumb.png)
[贝叶斯岭回归](https://scikit-learn.org/stable/auto_examples/linear_model/plot_bayesian_ridge.html#sphx-glr-auto-examples-linear-model-plot-bayesian-ridge-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id165) | ![](img/sphx_glr_plot_sparse_logistic_regression_20newsgroups_thumb.png)
[20newgroups上的多类稀疏逻辑回归](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sparse_logistic_regression_20newsgroups.html#sphx-glr-auto-examples-linear-model-plot-sparse-logistic-regression-20newsgroups-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id166) | ![](img/sphx_glr_plot_lasso_model_selection_thumb.png)
[套索模型选择：交叉验证/ AIC /](https://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_model_selection.html#sphx-glr-auto-examples-linear-model-plot-lasso-model-selection-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id167) | | ![](img/sphx_glr_plot_sgd_early_stopping_thumb.png)
[早期停止随机梯度下降的](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_early_stopping.html#sphx-glr-auto-examples-linear-model-plot-sgd-early-stopping-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id168) | @@ -193,7 +193,7 @@ scikit-learn 的 Miscellaneous 和入门示例。 | | | | | | -- | -- | -- | -- | -| ![](img/sphx_glr_plot_swissroll_thumb.png)
[使用LLE减少瑞士卷](https://scikit-learn.org/stable/auto_examples/manifold/plot_swissroll.html#sphx-glr-auto-examples-manifold-plot-swissroll-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id172) | ![](img/sphx_glr_plot_compare_methods_thumb.png)
[流形学习方法的比较](https://scikit-learn.org/stable/auto_examples/manifold/plot_compare_methods.html#sphx-glr-auto-examples-manifold-plot-compare-methods-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id173) | ![](img/sphx_glr_plot_mds_thumb.png)
[多维缩放](https://scikit-learn.org/stable/auto_examples/manifold/plot_mds.html#sphx-glr-auto-examples-manifold-plot-mds-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id174) | ![](img/sphx_glr_plot_t_sne_perplexity_thumb.png)
[叔SNE：各种困惑值对形状的影响](https://scikit-learn.org/stable/auto_examples/manifold/plot_t_sne_perplexity.html#sphx-glr-auto-examples-manifold-plot-t-sne-perplexity-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id175) | +| ![](img/sphx_glr_plot_swissroll_thumb.png)
[使用LLE减少瑞士卷](https://scikit-learn.org/stable/auto_examples/manifold/plot_swissroll.html#sphx-glr-auto-examples-manifold-plot-swissroll-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id172) | ![](img/sphx_glr_plot_compare_methods_thumb.png)
[流形学习方法的比较](https://scikit-learn.org/stable/auto_examples/manifold/plot_compare_methods.html#sphx-glr-auto-examples-manifold-plot-compare-methods-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id173) | ![](img/sphx_glr_plot_mds_thumb.png)
[多维缩放](https://scikit-learn.org/stable/auto_examples/manifold/plot_mds.html#sphx-glr-auto-examples-manifold-plot-mds-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id174) | ![](img/sphx_glr_plot_t_sne_perplexity_thumb.png)
[叔SNE：各种困惑值对形状的影响](https://scikit-learn.org/stable/auto_examples/manifold/plot_t_sne_perplexity.html#sphx-glr-auto-examples-manifold-plot-t-sne-perplexity-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id175) | | ![](img/sphx_glr_plot_manifold_sphere_thumb.png)
[截断球面上的流形学习方法](https://scikit-learn.org/stable/auto_examples/manifold/plot_manifold_sphere.html#sphx-glr-auto-examples-manifold-plot-manifold-sphere-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id176) | ![](img/sphx_glr_plot_lle_digits_thumb.png)
[手写数字流形学习：局部线性嵌入，Isomap…](https://scikit-learn.org/stable/auto_examples/manifold/plot_lle_digits.html#sphx-glr-auto-examples-manifold-plot-lle-digits-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id177) | @@ -212,9 +212,9 @@ scikit-learn 的 Miscellaneous 和入门示例。 | | | | | | -- | -- | -- | -- | -| ![](img/sphx_glr_plot_cv_predict_thumb.png)
[绘制交叉验证的预测](https://scikit-learn.org/stable/auto_examples/model_selection/plot_cv_predict.html#sphx-glr-auto-examples-model-selection-plot-cv-predict-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id180) | ![](img/sphx_glr_plot_confusion_matrix_thumb.png)
[混淆矩阵](https://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html#sphx-glr-auto-examples-model-selection-plot-confusion-matrix-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id181) | ![](img/sphx_glr_plot_validation_curve_thumb.png)
[绘图验证曲线](https://scikit-learn.org/stable/auto_examples/model_selection/plot_validation_curve.html#sphx-glr-auto-examples-model-selection-plot-validation-curve-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id182) | ![](img/sphx_glr_plot_underfitting_overfitting_thumb.png)
[拟合不足与拟合过度](https://scikit-learn.org/stable/auto_examples/model_selection/plot_underfitting_overfitting.html#sphx-glr-auto-examples-model-selection-plot-underfitting-overfitting-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id183) | -| ![](img/sphx_glr_plot_grid_search_digits_thumb.png)
[使用带有交叉验证的网格搜索进行参数估计](https://scikit-learn.org/stable/auto_examples/model_selection/plot_grid_search_digits.html#sphx-glr-auto-examples-model-selection-plot-grid-search-digits-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id184) | ![](img/sphx_glr_plot_randomized_search_thumb.png)
[对于比较估计超参数随机搜索和网格搜索](https://scikit-learn.org/stable/auto_examples/model_selection/plot_randomized_search.html#sphx-glr-auto-examples-model-selection-plot-randomized-search-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id185) | ![](img/sphx_glr_plot_train_error_vs_test_error_thumb.png)
[训练错误与测试错误](https://scikit-learn.org/stable/auto_examples/model_selection/plot_train_error_vs_test_error.html#sphx-glr-auto-examples-model-selection-plot-train-error-vs-test-error-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id186) | ![](img/sphx_glr_plot_roc_crossval_thumb.png)
[具有交叉验证的接收器操作特性（ROC）](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc_crossval.html#sphx-glr-auto-examples-model-selection-plot-roc-crossval-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id187) | -| ![](img/sphx_glr_plot_nested_cross_validation_iris_thumb.png)
[嵌套与非嵌套交叉验证](https://scikit-learn.org/stable/auto_examples/model_selection/plot_nested_cross_validation_iris.html#sphx-glr-auto-examples-model-selection-plot-nested-cross-validation-iris-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id188) | ![](img/sphx_glr_plot_multi_metric_evaluation_thumb.png)
[在cross_val_score和GridSearchCV上进行多指标评估的演示](https://scikit-learn.org/stable/auto_examples/model_selection/plot_multi_metric_evaluation.html#sphx-glr-auto-examples-model-selection-plot-multi-metric-evaluation-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id189) | ![](img/sphx_glr_grid_search_text_feature_extraction_thumb.png)
[用于文本特征提取和评估的示例管道](https://scikit-learn.org/stable/auto_examples/model_selection/grid_search_text_feature_extraction.html#sphx-glr-auto-examples-model-selection-grid-search-text-feature-extraction-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id190) | ![](img/sphx_glr_plot_grid_search_refit_callable_thumb.png)
[平衡模型的复杂性和交叉验证的分数](https://scikit-learn.org/stable/auto_examples/model_selection/plot_grid_search_refit_callable.html#sphx-glr-auto-examples-model-selection-plot-grid-search-refit-callable-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id191) | +| ![](img/sphx_glr_plot_cv_predict_thumb.png)
[绘制交叉验证的预测](https://scikit-learn.org/stable/auto_examples/model_selection/plot_cv_predict.html#sphx-glr-auto-examples-model-selection-plot-cv-predict-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id180) | ![](img/sphx_glr_plot_confusion_matrix_thumb.png)
[混淆矩阵](https://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html#sphx-glr-auto-examples-model-selection-plot-confusion-matrix-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id181) | ![](img/sphx_glr_plot_validation_curve_thumb.png)
[绘图验证曲线](https://scikit-learn.org/stable/auto_examples/model_selection/plot_validation_curve.html#sphx-glr-auto-examples-model-selection-plot-validation-curve-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id182) | ![](img/sphx_glr_plot_underfitting_overfitting_thumb.png)
[拟合不足与拟合过度](https://scikit-learn.org/stable/auto_examples/model_selection/plot_underfitting_overfitting.html#sphx-glr-auto-examples-model-selection-plot-underfitting-overfitting-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id183) | +| ![](img/sphx_glr_plot_grid_search_digits_thumb.png)
[使用带有交叉验证的网格搜索进行参数估计](https://scikit-learn.org/stable/auto_examples/model_selection/plot_grid_search_digits.html#sphx-glr-auto-examples-model-selection-plot-grid-search-digits-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id184) | ![](img/sphx_glr_plot_randomized_search_thumb.png)
[对于比较估计超参数随机搜索和网格搜索](https://scikit-learn.org/stable/auto_examples/model_selection/plot_randomized_search.html#sphx-glr-auto-examples-model-selection-plot-randomized-search-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id185) | ![](img/sphx_glr_plot_train_error_vs_test_error_thumb.png)
[训练错误与测试错误](https://scikit-learn.org/stable/auto_examples/model_selection/plot_train_error_vs_test_error.html#sphx-glr-auto-examples-model-selection-plot-train-error-vs-test-error-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id186) | ![](img/sphx_glr_plot_roc_crossval_thumb.png)
[具有交叉验证的接收器操作特性（ROC）](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc_crossval.html#sphx-glr-auto-examples-model-selection-plot-roc-crossval-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id187) | +| ![](img/sphx_glr_plot_nested_cross_validation_iris_thumb.png)
[嵌套与非嵌套交叉验证](https://scikit-learn.org/stable/auto_examples/model_selection/plot_nested_cross_validation_iris.html#sphx-glr-auto-examples-model-selection-plot-nested-cross-validation-iris-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id188) | ![](img/sphx_glr_plot_multi_metric_evaluation_thumb.png)
[在cross_val_score和GridSearchCV上进行多指标评估的演示](https://scikit-learn.org/stable/auto_examples/model_selection/plot_multi_metric_evaluation.html#sphx-glr-auto-examples-model-selection-plot-multi-metric-evaluation-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id189) | ![](img/sphx_glr_grid_search_text_feature_extraction_thumb.png)
[用于文本特征提取和评估的示例管道](https://scikit-learn.org/stable/auto_examples/model_selection/grid_search_text_feature_extraction.html#sphx-glr-auto-examples-model-selection-grid-search-text-feature-extraction-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id190) | ![](img/sphx_glr_plot_grid_search_refit_callable_thumb.png)
[平衡模型的复杂性和交叉验证的分数](https://scikit-learn.org/stable/auto_examples/model_selection/plot_grid_search_refit_callable.html#sphx-glr-auto-examples-model-selection-plot-grid-search-refit-callable-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id191) | | ![](img/sphx_glr_plot_cv_indices_thumb.png)
[在scikit-learn中可视化交叉验证行为](https://scikit-learn.org/stable/auto_examples/model_selection/plot_cv_indices.html#sphx-glr-auto-examples-model-selection-plot-cv-indices-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id192) | ![](img/sphx_glr_plot_roc_thumb.png)
[接收器工作特性（ROC）](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html#sphx-glr-auto-examples-model-selection-plot-roc-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id193) | ![](img/sphx_glr_plot_precision_recall_thumb.png)
[精密召回](https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html#sphx-glr-auto-examples-model-selection-plot-precision-recall-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id194) | ![](img/sphx_glr_plot_learning_curve_thumb.png)
[绘制学习曲线](https://scikit-learn.org/stable/auto_examples/model_selection/plot_learning_curve.html#sphx-glr-auto-examples-model-selection-plot-learning-curve-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id195) | @@ -233,9 +233,9 @@ scikit-learn 的 Miscellaneous 和入门示例。 | | | | | | -- | -- | -- | -- | -| ![](img/sphx_glr_plot_regression_thumb.png)
[最近邻居回归](https://scikit-learn.org/stable/auto_examples/neighbors/plot_regression.html#sphx-glr-auto-examples-neighbors-plot-regression-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id197) | ![](img/sphx_glr_plot_lof_outlier_detection_thumb.png)
[使用局部离群因子（LOF）进行离群检测](https://scikit-learn.org/stable/auto_examples/neighbors/plot_lof_outlier_detection.html#sphx-glr-auto-examples-neighbors-plot-lof-outlier-detection-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id198) | ![](img/sphx_glr_plot_classification_thumb.png)
[最近邻居分类](https://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html#sphx-glr-auto-examples-neighbors-plot-classification-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id199) | ![](img/sphx_glr_plot_nearest_centroid_thumb.png)
[最近质心分类](https://scikit-learn.org/stable/auto_examples/neighbors/plot_nearest_centroid.html#sphx-glr-auto-examples-neighbors-plot-nearest-centroid-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id200) | -| ![](img/sphx_glr_plot_digits_kde_sampling_thumb.png)
[核密度估计](https://scikit-learn.org/stable/auto_examples/neighbors/plot_digits_kde_sampling.html#sphx-glr-auto-examples-neighbors-plot-digits-kde-sampling-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id201) | ![](img/sphx_glr_plot_caching_nearest_neighbors_thumb.png)
[缓存最近的邻居](https://scikit-learn.org/stable/auto_examples/neighbors/plot_caching_nearest_neighbors.html#sphx-glr-auto-examples-neighbors-plot-caching-nearest-neighbors-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id202) | ![](img/sphx_glr_plot_nca_illustration_thumb.png)
[邻域成分分析图](https://scikit-learn.org/stable/auto_examples/neighbors/plot_nca_illustration.html#sphx-glr-auto-examples-neighbors-plot-nca-illustration-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id203) | ![](img/sphx_glr_plot_lof_novelty_detection_thumb.png)
[具有局部异常值（LOF）的新颖性检测](https://scikit-learn.org/stable/auto_examples/neighbors/plot_lof_novelty_detection.html#sphx-glr-auto-examples-neighbors-plot-lof-novelty-detection-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id204) | -| ![](img/sphx_glr_plot_nca_classification_thumb.png)
[比较具有和不具有邻域分量分析的最近邻域](https://scikit-learn.org/stable/auto_examples/neighbors/plot_nca_classification.html#sphx-glr-auto-examples-neighbors-plot-nca-classification-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id205) | ![](img/sphx_glr_plot_nca_dim_reduction_thumb.png)
[使用邻域分量分析进行](https://scikit-learn.org/stable/auto_examples/neighbors/plot_nca_dim_reduction.html#sphx-glr-auto-examples-neighbors-plot-nca-dim-reduction-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id206)[维](https://scikit-learn.org/stable/auto_examples/neighbors/plot_nca_dim_reduction.html#sphx-glr-auto-examples-neighbors-plot-nca-dim-reduction-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id206) | ![](img/sphx_glr_plot_species_kde_thumb.png)
[物种分布的核密度估计](https://scikit-learn.org/stable/auto_examples/neighbors/plot_species_kde.html#sphx-glr-auto-examples-neighbors-plot-species-kde-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id207) | ![](img/sphx_glr_plot_kde_1d_thumb.png)
[简单的1D内核密度估计](https://scikit-learn.org/stable/auto_examples/neighbors/plot_kde_1d.html#sphx-glr-auto-examples-neighbors-plot-kde-1d-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id208) | +| ![](img/sphx_glr_plot_regression_thumb.png)
[最近邻居回归](https://scikit-learn.org/stable/auto_examples/neighbors/plot_regression.html#sphx-glr-auto-examples-neighbors-plot-regression-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id197) | ![](img/sphx_glr_plot_lof_outlier_detection_thumb.png)
[使用局部离群因子（LOF）进行离群检测](https://scikit-learn.org/stable/auto_examples/neighbors/plot_lof_outlier_detection.html#sphx-glr-auto-examples-neighbors-plot-lof-outlier-detection-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id198) | ![](img/sphx_glr_plot_classification_thumb.png)
[最近邻居分类](https://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html#sphx-glr-auto-examples-neighbors-plot-classification-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id199) | ![](img/sphx_glr_plot_nearest_centroid_thumb.png)
[最近质心分类](https://scikit-learn.org/stable/auto_examples/neighbors/plot_nearest_centroid.html#sphx-glr-auto-examples-neighbors-plot-nearest-centroid-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id200) | +| ![](img/sphx_glr_plot_digits_kde_sampling_thumb.png)
[核密度估计](https://scikit-learn.org/stable/auto_examples/neighbors/plot_digits_kde_sampling.html#sphx-glr-auto-examples-neighbors-plot-digits-kde-sampling-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id201) | ![](img/sphx_glr_plot_caching_nearest_neighbors_thumb.png)
[缓存最近的邻居](https://scikit-learn.org/stable/auto_examples/neighbors/plot_caching_nearest_neighbors.html#sphx-glr-auto-examples-neighbors-plot-caching-nearest-neighbors-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id202) | ![](img/sphx_glr_plot_nca_illustration_thumb.png)
[邻域成分分析图](https://scikit-learn.org/stable/auto_examples/neighbors/plot_nca_illustration.html#sphx-glr-auto-examples-neighbors-plot-nca-illustration-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id203) | ![](img/sphx_glr_plot_lof_novelty_detection_thumb.png)
[具有局部异常值（LOF）的新颖性检测](https://scikit-learn.org/stable/auto_examples/neighbors/plot_lof_novelty_detection.html#sphx-glr-auto-examples-neighbors-plot-lof-novelty-detection-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id204) | +| ![](img/sphx_glr_plot_nca_classification_thumb.png)
[比较具有和不具有邻域分量分析的最近邻域](https://scikit-learn.org/stable/auto_examples/neighbors/plot_nca_classification.html#sphx-glr-auto-examples-neighbors-plot-nca-classification-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id205) | ![](img/sphx_glr_plot_nca_dim_reduction_thumb.png)
[使用邻域分量分析进行](https://scikit-learn.org/stable/auto_examples/neighbors/plot_nca_dim_reduction.html#sphx-glr-auto-examples-neighbors-plot-nca-dim-reduction-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id206)[维](https://scikit-learn.org/stable/auto_examples/neighbors/plot_nca_dim_reduction.html#sphx-glr-auto-examples-neighbors-plot-nca-dim-reduction-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id206) | ![](img/sphx_glr_plot_species_kde_thumb.png)
[物种分布的核密度估计](https://scikit-learn.org/stable/auto_examples/neighbors/plot_species_kde.html#sphx-glr-auto-examples-neighbors-plot-species-kde-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id207) | ![](img/sphx_glr_plot_kde_1d_thumb.png)
[简单的1D内核密度估计](https://scikit-learn.org/stable/auto_examples/neighbors/plot_kde_1d.html#sphx-glr-auto-examples-neighbors-plot-kde-1d-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id208) | | ![](img/sphx_glr_approximate_nearest_neighbors_thumb.png)
[TSNE中的近似最近邻居](https://scikit-learn.org/stable/auto_examples/neighbors/approximate_nearest_neighbors.html#sphx-glr-auto-examples-neighbors-approximate-nearest-neighbors-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id209) | @@ -254,7 +254,7 @@ scikit-learn 的 Miscellaneous 和入门示例。 | | | | | | -- | -- | -- | -- | -| ![](img/sphx_glr_plot_feature_union_thumb.png)
[连结多个特征提取方法](https://scikit-learn.org/stable/auto_examples/compose/plot_feature_union.html#sphx-glr-auto-examples-compose-plot-feature-union-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id214) | ![](img/sphx_glr_plot_digits_pipe_thumb.png)
[流水线：链接PCA和逻辑回归](https://scikit-learn.org/stable/auto_examples/compose/plot_digits_pipe.html#sphx-glr-auto-examples-compose-plot-digits-pipe-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id215) | ![](img/sphx_glr_plot_column_transformer_mixed_types_thumb.png)
[混合类型的列转换器](https://scikit-learn.org/stable/auto_examples/compose/plot_column_transformer_mixed_types.html#sphx-glr-auto-examples-compose-plot-column-transformer-mixed-types-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id216) | ![](img/sphx_glr_plot_compare_reduction_thumb.png)
[使用Pipeline和GridSearchCV选择降维](https://scikit-learn.org/stable/auto_examples/compose/plot_compare_reduction.html#sphx-glr-auto-examples-compose-plot-compare-reduction-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id217) | +| ![](img/sphx_glr_plot_feature_union_thumb.png)
[连结多个特征提取方法](https://scikit-learn.org/stable/auto_examples/compose/plot_feature_union.html#sphx-glr-auto-examples-compose-plot-feature-union-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id214) | ![](img/sphx_glr_plot_digits_pipe_thumb.png)
[流水线：链接PCA和逻辑回归](https://scikit-learn.org/stable/auto_examples/compose/plot_digits_pipe.html#sphx-glr-auto-examples-compose-plot-digits-pipe-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id215) | ![](img/sphx_glr_plot_column_transformer_mixed_types_thumb.png)
[混合类型的列转换器](https://scikit-learn.org/stable/auto_examples/compose/plot_column_transformer_mixed_types.html#sphx-glr-auto-examples-compose-plot-column-transformer-mixed-types-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id216) | ![](img/sphx_glr_plot_compare_reduction_thumb.png)
[使用Pipeline和GridSearchCV选择降维](https://scikit-learn.org/stable/auto_examples/compose/plot_compare_reduction.html#sphx-glr-auto-examples-compose-plot-compare-reduction-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id217) | | ![](img/sphx_glr_plot_column_transformer_thumb.png)
[具有异构数据源的列转换器](https://scikit-learn.org/stable/auto_examples/compose/plot_column_transformer.html#sphx-glr-auto-examples-compose-plot-column-transformer-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id218) | ![](img/sphx_glr_plot_transformed_target_thumb.png)
[在回归模型中转换目标的效果](https://scikit-learn.org/stable/auto_examples/compose/plot_transformed_target.html#sphx-glr-auto-examples-compose-plot-transformed-target-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id219) | @@ -264,7 +264,7 @@ scikit-learn 的 Miscellaneous 和入门示例。 | | | | | | -- | -- | -- | -- | -| ![](img/sphx_glr_plot_function_transformer_thumb.png)
[使用FunctionTransformer选择列](https://scikit-learn.org/stable/auto_examples/preprocessing/plot_function_transformer.html#sphx-glr-auto-examples-preprocessing-plot-function-transformer-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id220) | ![](img/sphx_glr_plot_discretization_thumb.png)
[使用KBinsDiscretizer离散化连续特征](https://scikit-learn.org/stable/auto_examples/preprocessing/plot_discretization.html#sphx-glr-auto-examples-preprocessing-plot-discretization-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id221) | ![](img/sphx_glr_plot_discretization_strategies_thumb.png)
[演示KBinsDiscretizer的不同策略](https://scikit-learn.org/stable/auto_examples/preprocessing/plot_discretization_strategies.html#sphx-glr-auto-examples-preprocessing-plot-discretization-strategies-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id222) | ![](img/sphx_glr_plot_scaling_importance_thumb.png)
[特征缩放的重要性](https://scikit-learn.org/stable/auto_examples/preprocessing/plot_scaling_importance.html#sphx-glr-auto-examples-preprocessing-plot-scaling-importance-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id223) | +| ![](img/sphx_glr_plot_function_transformer_thumb.png)
[使用FunctionTransformer选择列](https://scikit-learn.org/stable/auto_examples/preprocessing/plot_function_transformer.html#sphx-glr-auto-examples-preprocessing-plot-function-transformer-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id220) | ![](img/sphx_glr_plot_discretization_thumb.png)
[使用KBinsDiscretizer离散化连续特征](https://scikit-learn.org/stable/auto_examples/preprocessing/plot_discretization.html#sphx-glr-auto-examples-preprocessing-plot-discretization-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id221) | ![](img/sphx_glr_plot_discretization_strategies_thumb.png)
[演示KBinsDiscretizer的不同策略](https://scikit-learn.org/stable/auto_examples/preprocessing/plot_discretization_strategies.html#sphx-glr-auto-examples-preprocessing-plot-discretization-strategies-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id222) | ![](img/sphx_glr_plot_scaling_importance_thumb.png)
[特征缩放的重要性](https://scikit-learn.org/stable/auto_examples/preprocessing/plot_scaling_importance.html#sphx-glr-auto-examples-preprocessing-plot-scaling-importance-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id223) | | ![](img/sphx_glr_plot_map_data_to_normal_thumb.png)
[地图数据正态分布](https://scikit-learn.org/stable/auto_examples/preprocessing/plot_map_data_to_normal.html#sphx-glr-auto-examples-preprocessing-plot-map-data-to-normal-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id224) | ![](img/sphx_glr_plot_discretization_classification_thumb.png)
[功能离散](https://scikit-learn.org/stable/auto_examples/preprocessing/plot_discretization_classification.html#sphx-glr-auto-examples-preprocessing-plot-discretization-classification-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id225) | ![](img/sphx_glr_plot_all_scaling_thumb.png)
[比较不同缩放器对数据与异常值的影响](https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html#sphx-glr-auto-examples-preprocessing-plot-all-scaling-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id226) | @@ -292,15 +292,15 @@ scikit-learn 的 Miscellaneous 和入门示例。 | | | | | | -- | -- | -- | -- | -| ![](img/sphx_glr_plot_svm_nonlinear_thumb.png)
[非线性](https://scikit-learn.org/stable/auto_examples/svm/plot_svm_nonlinear.html#sphx-glr-auto-examples-svm-plot-svm-nonlinear-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id232) | ![](img/sphx_glr_plot_separating_hyperplane_thumb.png)
[SVM：最大余量分隔超平面](https://scikit-learn.org/stable/auto_examples/svm/plot_separating_hyperplane.html#sphx-glr-auto-examples-svm-plot-separating-hyperplane-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id233) | ![](img/sphx_glr_plot_custom_kernel_thumb.png)
[具有自定义内核的](https://scikit-learn.org/stable/auto_examples/svm/plot_custom_kernel.html#sphx-glr-auto-examples-svm-plot-custom-kernel-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id234) | ![](img/sphx_glr_plot_linearsvc_support_vectors_thumb.png)
[在LinearSVC中绘制支持向量](https://scikit-learn.org/stable/auto_examples/svm/plot_linearsvc_support_vectors.html#sphx-glr-auto-examples-svm-plot-linearsvc-support-vectors-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id235) | -| ![](img/sphx_glr_plot_svm_tie_breaking_thumb.png)
[SVM中断示例](https://scikit-learn.org/stable/auto_examples/svm/plot_svm_tie_breaking.html#sphx-glr-auto-examples-svm-plot-svm-tie-breaking-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id236) | ![](img/sphx_glr_plot_weighted_samples_thumb.png)
[SVM：加权样本](https://scikit-learn.org/stable/auto_examples/svm/plot_weighted_samples.html#sphx-glr-auto-examples-svm-plot-weighted-samples-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id237) | ![](img/sphx_glr_plot_separating_hyperplane_unbalanced_thumb.png)
[SVM：为不平衡的类分离超平面](https://scikit-learn.org/stable/auto_examples/svm/plot_separating_hyperplane_unbalanced.html#sphx-glr-auto-examples-svm-plot-separating-hyperplane-unbalanced-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id238) | ![](img/sphx_glr_plot_svm_kernels_thumb.png)
[SVM内核](https://scikit-learn.org/stable/auto_examples/svm/plot_svm_kernels.html#sphx-glr-auto-examples-svm-plot-svm-kernels-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id239) | -| ![](img/sphx_glr_plot_svm_anova_thumb.png)
[SVM-Anova：具有单变量特征选择的](https://scikit-learn.org/stable/auto_examples/svm/plot_svm_anova.html#sphx-glr-auto-examples-svm-plot-svm-anova-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id240) | ![](img/sphx_glr_plot_svm_regression_thumb.png)
[使用线性和非线性内核支持向量回归（SVR）](https://scikit-learn.org/stable/auto_examples/svm/plot_svm_regression.html#sphx-glr-auto-examples-svm-plot-svm-regression-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id241) | ![](img/sphx_glr_plot_svm_margin_thumb.png)
[SVM保证金示例](https://scikit-learn.org/stable/auto_examples/svm/plot_svm_margin.html#sphx-glr-auto-examples-svm-plot-svm-margin-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id242) | ![](img/sphx_glr_plot_oneclass_thumb.png)
[具有非线性内核（RBF）的一类](https://scikit-learn.org/stable/auto_examples/svm/plot_oneclass.html#sphx-glr-auto-examples-svm-plot-oneclass-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id243) | +| ![](img/sphx_glr_plot_svm_nonlinear_thumb.png)
[非线性](https://scikit-learn.org/stable/auto_examples/svm/plot_svm_nonlinear.html#sphx-glr-auto-examples-svm-plot-svm-nonlinear-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id232) | ![](img/sphx_glr_plot_separating_hyperplane_thumb.png)
[SVM：最大余量分隔超平面](https://scikit-learn.org/stable/auto_examples/svm/plot_separating_hyperplane.html#sphx-glr-auto-examples-svm-plot-separating-hyperplane-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id233) | ![](img/sphx_glr_plot_custom_kernel_thumb.png)
[具有自定义内核的](https://scikit-learn.org/stable/auto_examples/svm/plot_custom_kernel.html#sphx-glr-auto-examples-svm-plot-custom-kernel-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id234) | ![](img/sphx_glr_plot_linearsvc_support_vectors_thumb.png)
[在LinearSVC中绘制支持向量](https://scikit-learn.org/stable/auto_examples/svm/plot_linearsvc_support_vectors.html#sphx-glr-auto-examples-svm-plot-linearsvc-support-vectors-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id235) | +| ![](img/sphx_glr_plot_svm_tie_breaking_thumb.png)
[SVM中断示例](https://scikit-learn.org/stable/auto_examples/svm/plot_svm_tie_breaking.html#sphx-glr-auto-examples-svm-plot-svm-tie-breaking-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id236) | ![](img/sphx_glr_plot_weighted_samples_thumb.png)
[SVM：加权样本](https://scikit-learn.org/stable/auto_examples/svm/plot_weighted_samples.html#sphx-glr-auto-examples-svm-plot-weighted-samples-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id237) | ![](img/sphx_glr_plot_separating_hyperplane_unbalanced_thumb.png)
[SVM：为不平衡的类分离超平面](https://scikit-learn.org/stable/auto_examples/svm/plot_separating_hyperplane_unbalanced.html#sphx-glr-auto-examples-svm-plot-separating-hyperplane-unbalanced-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id238) | ![](img/sphx_glr_plot_svm_kernels_thumb.png)
[SVM内核](https://scikit-learn.org/stable/auto_examples/svm/plot_svm_kernels.html#sphx-glr-auto-examples-svm-plot-svm-kernels-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id239) | +| ![](img/sphx_glr_plot_svm_anova_thumb.png)
[SVM-Anova：具有单变量特征选择的](https://scikit-learn.org/stable/auto_examples/svm/plot_svm_anova.html#sphx-glr-auto-examples-svm-plot-svm-anova-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id240) | ![](img/sphx_glr_plot_svm_regression_thumb.png)
[使用线性和非线性内核支持向量回归（SVR）](https://scikit-learn.org/stable/auto_examples/svm/plot_svm_regression.html#sphx-glr-auto-examples-svm-plot-svm-regression-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id241) | ![](img/sphx_glr_plot_svm_margin_thumb.png)
[SVM保证金示例](https://scikit-learn.org/stable/auto_examples/svm/plot_svm_margin.html#sphx-glr-auto-examples-svm-plot-svm-margin-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id242) | ![](img/sphx_glr_plot_oneclass_thumb.png)
[具有非线性内核（RBF）的一类](https://scikit-learn.org/stable/auto_examples/svm/plot_oneclass.html#sphx-glr-auto-examples-svm-plot-oneclass-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id243) | | ![](img/sphx_glr_plot_iris_svc_thumb.png)
[在虹膜数据集中绘制不同的SVM分类器](https://scikit-learn.org/stable/auto_examples/svm/plot_iris_svc.html#sphx-glr-auto-examples-svm-plot-iris-svc-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id244) | ![](img/sphx_glr_plot_svm_scale_c_thumb.png)
[扩展SVC的正则化参数](https://scikit-learn.org/stable/auto_examples/svm/plot_svm_scale_c.html#sphx-glr-auto-examples-svm-plot-svm-scale-c-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id245) | ![](img/sphx_glr_plot_rbf_parameters_thumb.png)
[RBF SVM参数](https://scikit-learn.org/stable/auto_examples/svm/plot_rbf_parameters.html#sphx-glr-auto-examples-svm-plot-rbf-parameters-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id246) | ## 教程练习 -教程练习 +教程练习。 | | | | | | -- | -- | -- | -- | diff --git a/docs/examples/SUMMARY.md b/docs/examples/SUMMARY.md index a025ff454d07d3da194ce49cf84488f40143a344..a48ed1e44438f461f379e1797c390def2a7f8c48 100644 --- a/docs/examples/SUMMARY.md +++ b/docs/examples/SUMMARY.md @@ -1,10 +1,38 @@ -* Miscellaneous 示例 +* 杂项示例 +* 双集群 +* 校准 +* 分类 +* 多集群 +* 协方差估计 +* 交叉分解 +* 数据集示例 +* 决策树 +* 分解 +* 集成方法 +* 基于真实数据集的示例 +* 特征选择 +* 高斯混合模型 +* 高斯机器学习过程 * 广义线性模型 - * [分类特征稀疏的文本](Generalized_Linear_Models/plot_document_classification_20newsgroups.md) - * [Lasso和Elastic Net(弹性网络)在稀疏信号上的表现](Generalized_Linear_Models/plot_lasso_and_elasticnet.md) - * [Lasso和Elastic Net(弹性网络)](Generalized_Linear_Models/plot_lasso_coordinate_descent_path.md) - * [Lasso模型选择:交叉验证 / AIC / BIC](Generalized_Linear_Models/plot_lasso_model_selection.md) - * [多任务Lasso实现联合特征选择](Generalized_Linear_Models/plot_multi_task_lasso_support.md) - * [线性回归](Generalized_Linear_Models/plot_ols.md) + * [Lasso 和弹性网络在稀疏信号上的表现](Generalized_Linear_Models/plot_lasso_and_elasticnet.md) + * [Lasso 和弹性网络](Generalized_Linear_Models/plot_lasso_coordinate_descent_path.md) + * [Lasso 模型选择：交叉验证 / AIC / BIC](Generalized_Linear_Models/plot_lasso_model_selection.md) + * [多任务 Lasso 实现联合特征选择](Generalized_Linear_Models/plot_multi_task_lasso_support.md) + * [线性回归示例](Generalized_Linear_Models/plot_ols.md) * [岭系数对回归系数的影响](Generalized_Linear_Models/plot_ridge_path.md) - * [压缩感知_断层重建](Generalized_Linear_Models/plot_tomography_l1_reconstruction.md) + * [压缩感知：用L1先验概率进行断层重建](Generalized_Linear_Models/plot_tomography_l1_reconstruction.md) +* 检查 +* 流行学习 +* 缺失值插补 +* 选型 +* 多输出方法 +* 最近邻 +* 神经网络 +* 管道和复合估计器 +* 预处理 +* 发布要点 +* 半监督分类 +* 支持向量机 +* 建成练习 +* 文本文档工作 + * [分类特征稀疏的文本](Generalized_Linear_Models/plot_document_classification_20newsgroups.md)