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...
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@@ -2,18 +2,18 @@
英文地址:
<https://scikit-learn.org/stable/auto_examples/index.html>
##
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
)
<br/>
[
紧凑的估计表示
](
https://scikit-learn.org/stable/auto_examples/plot_changed_only_pprint_parameter.html#sphx-glr-auto-examples-plot-changed-only-pprint-parameter-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id2) | !
[](
img/sphx_glr_plot_roc_curve_visualization_api_thumb.png
)
<br/>
[
带有可视化API的ROC曲线
](
https://scikit-learn.org/stable/auto_examples/plot_roc_curve_visualization_api.html#sphx-glr-auto-examples-plot-roc-curve-visualization-api-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id3) | !
[](
img/sphx_glr_plot_isotonic_regression_thumb.png
)
<br/>
[
序回归
](
https://scikit-learn.org/stable/auto_examples/plot_isotonic_regression.html#sphx-glr-auto-examples-plot-isotonic-regression-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id4) | !
[](
img/sphx_glr_plot_partial_dependence_visualization_api_thumb.png
)
<br/>
[
先进的绘图具有部分依赖
](
https://scikit-learn.org/stable/auto_examples/plot_partial_dependence_visualization_api.html#sphx-glr-auto-examples-plot-partial-dependence-visualization-api-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id5) |
| !
[](
img/sphx_glr_plot_multioutput_face_completion_thumb.png
)
<br/>
[
使用多输出估计器完成人脸
](
https://scikit-learn.org/stable/auto_examples/plot_multioutput_face_completion.html#sphx-glr-auto-examples-plot-multioutput-face-completion-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id6) | !
[](
img/sphx_glr_plot_multilabel_thumb.png
)
<br/>
[
多标签分类
](
https://scikit-learn.org/stable/auto_examples/plot_multilabel.html#sphx-glr-auto-examples-plot-multilabel-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id7) | !
[](
img/sphx_glr_plot_anomaly_comparison_thumb.png
)
<br/>
[
比较异常检测算法以对玩具数据集进行异常检测
](
https://scikit-learn.org/stable/auto_examples/plot_anomaly_comparison.html#sphx-glr-auto-examples-plot-anomaly-comparison-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id8) | !
[](
img/sphx_glr_plot_johnson_lindenstrauss_bound_thumb.png
)
<br/>
[
具有随机投影嵌入的Johnson-Lindenstrauss边界
](
https://scikit-learn.org/stable/auto_examples/plot_johnson_lindenstrauss_bound.html#sphx-glr-auto-examples-plot-johnson-lindenstrauss-bound-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id9) |
| !
[](
img/sphx_glr_plot_changed_only_pprint_parameter_thumb.png
)
<br/>
[
紧凑的估计表示
](
https://scikit-learn.org/stable/auto_examples/plot_changed_only_pprint_parameter.html#sphx-glr-auto-examples-plot-changed-only-pprint-parameter-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id2) | !
[](
img/sphx_glr_plot_roc_curve_visualization_api_thumb.png
)
<br/>
[
带有可视化API的ROC曲线
](
https://scikit-learn.org/stable/auto_examples/plot_roc_curve_visualization_api.html#sphx-glr-auto-examples-plot-roc-curve-visualization-api-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id3) | !
[](
img/sphx_glr_plot_isotonic_regression_thumb.png
)
<br/>
[
序回归
](
https://scikit-learn.org/stable/auto_examples/plot_isotonic_regression.html#sphx-glr-auto-examples-plot-isotonic-regression-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id4) | !
[](
img/sphx_glr_plot_partial_dependence_visualization_api_thumb.png
)
<br/>
[
先进的绘图具有部分依赖
](
https://scikit-learn.org/stable/auto_examples/plot_partial_dependence_visualization_api.html#sphx-glr-auto-examples-plot-partial-dependence-visualization-api-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id5) |
| !
[](
img/sphx_glr_plot_multioutput_face_completion_thumb.png
)
<br/>
[
使用多输出估计器完成人脸
](
https://scikit-learn.org/stable/auto_examples/plot_multioutput_face_completion.html#sphx-glr-auto-examples-plot-multioutput-face-completion-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id6) | !
[](
img/sphx_glr_plot_multilabel_thumb.png
)
<br/>
[
多标签分类
](
https://scikit-learn.org/stable/auto_examples/plot_multilabel.html#sphx-glr-auto-examples-plot-multilabel-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id7) | !
[](
img/sphx_glr_plot_anomaly_comparison_thumb.png
)
<br/>
[
比较异常检测算法以对玩具数据集进行异常检测
](
https://scikit-learn.org/stable/auto_examples/plot_anomaly_comparison.html#sphx-glr-auto-examples-plot-anomaly-comparison-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id8) | !
[](
img/sphx_glr_plot_johnson_lindenstrauss_bound_thumb.png
)
<br/>
[
具有随机投影嵌入的Johnson-Lindenstrauss边界
](
https://scikit-learn.org/stable/auto_examples/plot_johnson_lindenstrauss_bound.html#sphx-glr-auto-examples-plot-johnson-lindenstrauss-bound-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id9) |
| !
[](
img/sphx_glr_plot_kernel_ridge_regression_thumb.png
)
<br/>
[
内核岭回归和SVR的比较
](
https://scikit-learn.org/stable/auto_examples/plot_kernel_ridge_regression.html#sphx-glr-auto-examples-plot-kernel-ridge-regression-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id10) | !
[](
img/sphx_glr_plot_kernel_approximation_thumb.png
)
<br/>
[
RBF内核的显式特征图逼近
](
https://scikit-learn.org/stable/auto_examples/plot_kernel_approximation.html#sphx-glr-auto-examples-plot-kernel-approximation-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id11) |
##
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
)
<br/>
[
分类法线和收缩线线性判别分析
](
https://scikit-learn.org/stable/auto_examples/classification/plot_lda.html#sphx-glr-auto-examples-classification-plot-lda-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id19) | !
[](
img/sphx_glr_plot_digits_classification_thumb.png
)
<br/>
[
识别手写数字
](
https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html#sphx-glr-auto-examples-classification-plot-digits-classification-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id20) | !
[](
img/sphx_glr_plot_classification_probability_thumb.png
)
<br/>
[
情节分类概率
](
https://scikit-learn.org/stable/auto_examples/classification/plot_classification_probability.html#sphx-glr-auto-examples-classification-plot-classification-probability-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id21) | !
[](
img/sphx_glr_plot_classifier_comparison_thumb.png
)
<br/>
[
分类器比较
](
https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html#sphx-glr-auto-examples-classification-plot-classifier-comparison-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id22) |
| !
[](
img/sphx_glr_plot_lda_thumb.png
)
<br/>
[
分类法线和收缩线线性判别分析
](
https://scikit-learn.org/stable/auto_examples/classification/plot_lda.html#sphx-glr-auto-examples-classification-plot-lda-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id19) | !
[](
img/sphx_glr_plot_digits_classification_thumb.png
)
<br/>
[
识别手写数字
](
https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html#sphx-glr-auto-examples-classification-plot-digits-classification-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id20) | !
[](
img/sphx_glr_plot_classification_probability_thumb.png
)
<br/>
[
情节分类概率
](
https://scikit-learn.org/stable/auto_examples/classification/plot_classification_probability.html#sphx-glr-auto-examples-classification-plot-classification-probability-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id21) | !
[](
img/sphx_glr_plot_classifier_comparison_thumb.png
)
<br/>
[
分类器比较
](
https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html#sphx-glr-auto-examples-classification-plot-classifier-comparison-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id22) |
| !
[](
img/sphx_glr_plot_lda_qda_thumb.png
)
<br/>
[
线性和二次判别分析与协方差椭球
](
https://scikit-learn.org/stable/auto_examples/classification/plot_lda_qda.html#sphx-glr-auto-examples-classification-plot-lda-qda-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id23) |
## 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
)
<br/>
[
绘制层次聚类树状图
](
https://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_dendrogram.html#sphx-glr-auto-examples-cluster-plot-agglomerative-dendrogram-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id24) | !
[](
img/sphx_glr_plot_digits_agglomeration_thumb.png
)
<br/>
[
功能集聚
](
https://scikit-learn.org/stable/auto_examples/cluster/plot_digits_agglomeration.html#sphx-glr-auto-examples-cluster-plot-digits-agglomeration-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id25) | !
[](
img/sphx_glr_plot_mean_shift_thumb.png
)
<br/>
[
均值漂移聚类算法的演示
](
https://scikit-learn.org/stable/auto_examples/cluster/plot_mean_shift.html#sphx-glr-auto-examples-cluster-plot-mean-shift-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id26) | !
[](
img/sphx_glr_plot_kmeans_assumptions_thumb.png
)
<br/>
[
的k均值假设示范
](
https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_assumptions.html#sphx-glr-auto-examples-cluster-plot-kmeans-assumptions-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id27) |
| !
[](
img/sphx_glr_plot_dict_face_patches_thumb.png
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
绘制层次聚类树状图
](
https://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_dendrogram.html#sphx-glr-auto-examples-cluster-plot-agglomerative-dendrogram-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id24) | !
[](
img/sphx_glr_plot_digits_agglomeration_thumb.png
)
<br/>
[
功能集聚
](
https://scikit-learn.org/stable/auto_examples/cluster/plot_digits_agglomeration.html#sphx-glr-auto-examples-cluster-plot-digits-agglomeration-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id25) | !
[](
img/sphx_glr_plot_mean_shift_thumb.png
)
<br/>
[
均值漂移聚类算法的演示
](
https://scikit-learn.org/stable/auto_examples/cluster/plot_mean_shift.html#sphx-glr-auto-examples-cluster-plot-mean-shift-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id26) | !
[](
img/sphx_glr_plot_kmeans_assumptions_thumb.png
)
<br/>
[
的k均值假设示范
](
https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_assumptions.html#sphx-glr-auto-examples-cluster-plot-kmeans-assumptions-py
)[]
(https://scikit-learn.org/stable/auto_examples/index.html#id27) |
| !
[](
img/sphx_glr_plot_dict_face_patches_thumb.png
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
)
<br/>
[
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
。
| | | | |
| -- | -- | -- | -- |
...
...
docs/examples/SUMMARY.md
浏览文件 @
c24d580c
*
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
)
编辑
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