基于模型和序列的特征选择 (../examples/feature_selection/plot_select_from_model_diabetes.py) |
00:55.641 |
0.0 |
比较随机森林和直方图梯度提升模型 (../examples/ensemble/plot_forest_hist_grad_boosting_comparison.py) |
00:54.432 |
0.0 |
异常点检测估计器评估 (../examples/miscellaneous/plot_outlier_detection_bench.py) |
00:44.039 |
0.0 |
使用Pipeline和GridSearchCV选择降维方法 (../examples/compose/plot_compare_reduction.py) |
00:40.953 |
0.0 |
后验调整决策函数的截止点 (../examples/model_selection/plot_tuned_decision_threshold.py) |
00:31.288 |
0.0 |
文本特征提取与评估示例管道 (../examples/model_selection/plot_grid_search_text_feature_extraction.py) |
00:26.573 |
0.0 |
使用字典学习进行图像去噪 (../examples/decomposition/plot_image_denoising.py) |
00:25.050 |
0.0 |
随机梯度下降的早期停止 (../examples/linear_model/plot_sgd_early_stopping.py) |
00:25.012 |
0.0 |
为成本敏感学习后验调整决策阈值 (../examples/model_selection/plot_cost_sensitive_learning.py) |
00:24.389 |
0.0 |
绘制学习曲线和检查模型可伸缩性 (../examples/model_selection/plot_learning_curve.py) |
00:23.630 |
0.0 |
部分依赖图和个体条件期望图 (../examples/inspection/plot_partial_dependence.py) |
00:22.278 |
0.0 |
目标编码器与其他编码器比较 (../examples/preprocessing/plot_target_encoder.py) |
00:22.041 |
0.0 |
使用多项式核近似的可伸缩学习 (../examples/kernel_approximation/plot_scalable_poly_kernels.py) |
00:21.869 |
0.0 |
使用堆叠组合预测器 (../examples/ensemble/plot_stack_predictors.py) |
00:21.747 |
0.0 |
手写数字上的流形学习：局部线性嵌入、Isomap… (../examples/manifold/plot_lle_digits.py) |
00:19.868 |
0.0 |
直方图梯度提升树中的特征 (../examples/ensemble/plot_hgbt_regression.py) |
00:19.658 |
0.0 |
泊松回归和非正态损失 (../examples/linear_model/plot_poisson_regression_non_normal_loss.py) |
00:19.324 |
0.0 |
预测延迟 (../examples/applications/plot_prediction_latency.py) |
00:18.815 |
0.0 |
多类训练元估计器概述 (../examples/multiclass/plot_multiclass_overview.py) |
00:18.752 |
0.0 |
平衡模型复杂性和交叉验证分数 (../examples/model_selection/plot_grid_search_refit_callable.py) |
00:17.747 |
0.0 |
缩放SVC的正则化参数 (../examples/svm/plot_svm_scale_c.py) |
00:17.612 |
0.0 |
瑞士卷和瑞士孔降维 (../examples/manifold/plot_swissroll.py) |
00:16.853 |
0.0 |
HDBSCAN聚类算法演示 (../examples/cluster/plot_hdbscan.py) |
00:14.147 |
0.0 |
时间相关特征工程 (../examples/applications/plot_cyclical_feature_engineering.py) |
00:13.663 |
0.0 |
scikit-learn 0.24 发布亮点 (../examples/release_highlights/plot_release_highlights_0_24_0.py) |
00:12.897 |
0.0 |
线性模型系数解释中的常见误区 (../examples/inspection/plot_linear_model_coefficient_interpretation.py) |
00:12.594 |
0.0 |
流形学习方法比较 (../examples/manifold/plot_compare_methods.py) |
00:12.349 |
0.0 |
使用置换检验分类分数的显著性 (../examples/model_selection/plot_permutation_tests_for_classification.py) |
00:11.711 |
0.0 |
使用非负矩阵分解和潜在狄利克雷分配进行主题提取 (../examples/applications/plot_topics_extraction_with_nmf_lda.py) |
00:11.584 |
0.0 |
使用随机投影进行嵌入的Johnson-Lindenstrauss界 (../examples/miscellaneous/plot_johnson_lindenstrauss_bound.py) |
00:10.618 |
0.0 |
梯度提升回归的预测区间 (../examples/ensemble/plot_gradient_boosting_quantile.py) |
00:10.113 |
0.0 |
时间序列预测的滞后特征 (../examples/applications/plot_time_series_lagged_features.py) |
00:09.613 |
0.0 |
交叉验证网格搜索的自定义重新拟合策略 (../examples/model_selection/plot_grid_search_digits.py) |
00:09.533 |
0.0 |
梯度提升袋外估计 (../examples/ensemble/plot_gradient_boosting_oob.py) |
00:09.342 |
0.0 |
构建估计器前填充缺失值 (../examples/impute/plot_missing_values.py) |
00:08.551 |
0.0 |
比较不同缩放器对含异常值数据的影响 (../examples/preprocessing/plot_all_scaling.py) |
00:08.537 |
0.0 |
文本数据的核外分类 (../examples/applications/plot_out_of_core_classification.py) |
00:08.501 |
0.0 |
使用多项式逻辑回归+L1对MNIST进行分类 (../examples/linear_model/plot_sparse_logistic_regression_mnist.py) |
00:08.100 |
0.0 |
梯度提升正则化 (../examples/ensemble/plot_gradient_boosting_regularization.py) |
00:07.957 |
0.0 |
核岭回归和SVR的比较 (../examples/miscellaneous/plot_kernel_ridge_regression.py) |
00:07.914 |
0.0 |
截断球体上的流形学习方法 (../examples/manifold/plot_manifold_sphere.py) |
00:07.901 |
0.0 |
cross_val_score 和 GridSearchCV 的多指标评估演示 (../examples/model_selection/plot_multi_metric_evaluation.py) |
00:07.888 |
0.0 |
用于分类的普通、Ledoit-Wolf 和 OAS 线性判别分析 (../examples/classification/plot_lda.py) |
00:07.729 |
0.0 |
使用k-means对文本文档进行聚类 (../examples/text/plot_document_clustering.py) |
00:07.550 |
0.0 |
保险索赔上的Tweedie回归 (../examples/linear_model/plot_tweedie_regression_insurance_claims.py) |
00:07.329 |
0.0 |
人脸数据集分解 (../examples/decomposition/plot_faces_decomposition.py) |
00:06.996 |
0.0 |
使用核PCA进行图像去噪 (../examples/applications/plot_digits_denoising.py) |
00:06.834 |
0.0 |
网格搜索和逐次减半的比较 (../examples/model_selection/plot_successive_halving_heatmap.py) |
00:06.831 |
0.0 |
物种分布建模 (../examples/applications/plot_species_distribution_modeling.py) |
00:06.623 |
0.0 |
变分贝叶斯高斯混合的集中先验类型分析 (../examples/mixture/plot_concentration_prior.py) |
00:06.602 |
0.0 |
文本数据集上的半监督分类 (../examples/semi_supervised/plot_semi_supervised_newsgroups.py) |
00:06.562 |
0.0 |
使用稀疏特征对文本文档进行分类 (../examples/text/plot_document_classification_20newsgroups.py) |
00:06.520 |
0.0 |
MNIST上MLP权重的可视化 (../examples/neural_networks/plot_mnist_filters.py) |
00:06.323 |
0.0 |
在鸢尾花数据集上绘制树集成模型的决策面 (../examples/ensemble/plot_forest_iris.py) |
00:06.294 |
0.0 |
嵌套交叉验证与非嵌套交叉验证 (../examples/model_selection/plot_nested_cross_validation_iris.py) |
00:06.277 |
0.0 |
使用特征脸和SVM进行人脸识别示例 (../examples/applications/plot_face_recognition.py) |
00:06.254 |
0.0 |
使用谱协同聚类算法对文档进行双向聚类 (../examples/bicluster/plot_bicluster_newsgroups.py) |
00:06.193 |
0.0 |
高斯过程回归（GPR）估计数据噪声水平的能力 (../examples/gaussian_process/plot_gpr_noisy.py) |
00:05.820 |
0.0 |
在玩具数据集上比较不同的聚类算法 (../examples/cluster/plot_cluster_comparison.py) |
00:05.591 |
0.0 |
在20newgroups数据集上进行多类稀疏逻辑回归 (../examples/linear_model/plot_sparse_logistic_regression_20newsgroups.py) |
00:05.469 |
0.0 |
使用线性核和非线性核的支持向量回归 (SVR) (../examples/svm/plot_svm_regression.py) |
00:05.393 |
0.0 |
使用IterativeImputer的变体填充缺失值 (../examples/impute/plot_iterative_imputer_variants_comparison.py) |
00:05.374 |
0.0 |
FeatureHasher和DictVectorizer比较 (../examples/text/plot_hashing_vs_dict_vectorizer.py) |
00:05.316 |
0.0 |
scikit-learn 1.2 发布亮点 (../examples/release_highlights/plot_release_highlights_1_2_0.py) |
00:05.183 |
0.0 |
模型复杂性影响 (../examples/applications/plot_model_complexity_influence.py) |
00:05.164 |
0.0 |
自训练中阈值变化的影响 (../examples/semi_supervised/plot_self_training_varying_threshold.py) |
00:04.996 |
0.0 |
RBF SVM参数 (../examples/svm/plot_rbf_parameters.py) |
00:04.929 |
0.0 |
将希腊硬币图片分割成区域 (../examples/cluster/plot_coin_segmentation.py) |
00:04.828 |
0.0 |
逐次减半迭代 (../examples/model_selection/plot_successive_halving_iterations.py) |
00:04.778 |
0.0 |
模型正则化对训练误差和测试误差的影响 (../examples/model_selection/plot_train_error_vs_test_error.py) |
00:04.730 |
0.0 |
多类AdaBoosted决策树 (../examples/ensemble/plot_adaboost_multiclass.py) |
00:04.388 |
0.0 |
使用高斯过程回归（GPR）预测Mona Loa数据集上的CO2水平 (../examples/gaussian_process/plot_gpr_co2.py) |
00:04.369 |
0.0 |
核岭回归和高斯过程回归的比较 (../examples/gaussian_process/plot_compare_gpr_krr.py) |
00:04.333 |
0.0 |
多重共线性或相关特征的置换重要性 (../examples/inspection/plot_permutation_importance_multicollinear.py) |
00:04.272 |
0.0 |
置换重要性与随机森林特征重要性 (MDI) 的比较 (../examples/inspection/plot_permutation_importance.py) |
00:04.207 |
0.0 |
核密度估计 (../examples/neighbors/plot_digits_kde_sampling.py) |
00:04.030 |
0.0 |
比较随机搜索和网格搜索进行超参数估计 (../examples/model_selection/plot_randomized_search.py) |
00:03.871 |
0.0 |
随机森林的OOB误差 (../examples/ensemble/plot_ensemble_oob.py) |
00:03.676 |
0.0 |
梯度提升中的类别特征支持 (../examples/ensemble/plot_gradient_boosting_categorical.py) |
00:03.595 |
0.0 |
特征离散化 (../examples/preprocessing/plot_discretization_classification.py) |
00:03.487 |
0.0 |
物种分布的核密度估计 (../examples/neighbors/plot_species_kde.py) |
00:03.383 |
0.0 |
比较BIRCH和MiniBatchKMeans (../examples/cluster/plot_birch_vs_minibatchkmeans.py) |
00:03.381 |
0.0 |
在玩具数据集上比较异常检测算法进行异常点检测 (../examples/miscellaneous/plot_anomaly_comparison.py) |
00:03.239 |
0.0 |
梯度提升中的早期停止 (../examples/ensemble/plot_gradient_boosting_early_stopping.py) |
00:03.175 |
0.0 |
递归特征消除 (../examples/feature_selection/plot_rfe_digits.py) |
00:03.174 |
0.0 |
t-SNE：不同困惑度值对形状的影响 (../examples/manifold/plot_t_sne_perplexity.py) |
00:03.074 |
0.0 |
分类器校准的比较 (../examples/calibration/plot_compare_calibration.py) |
00:02.941 |
0.0 |
使用概率PCA和因子分析（FA）进行模型选择 (../examples/decomposition/plot_pca_vs_fa_model_selection.py) |
00:02.888 |
0.0 |
使用树集成进行特征变换 (../examples/ensemble/plot_feature_transformation.py) |
00:02.661 |
0.0 |
绘制分类概率 (../examples/classification/plot_classification_probability.py) |
00:02.612 |
0.0 |
异构数据源的列转换器 (../examples/compose/plot_column_transformer.py) |
00:02.557 |
0.0 |
鸢尾花数据集上的高斯过程分类（GPC） (../examples/gaussian_process/plot_gpc_iris.py) |
00:02.556 |
0.0 |
比较MLPClassifier的随机学习策略 (../examples/neural_networks/plot_mlp_training_curves.py) |
00:02.556 |
0.0 |
受限玻尔兹曼机特征用于数字分类 (../examples/neural_networks/plot_rbm_logistic_classification.py) |
00:02.439 |
0.0 |
稳健协方差估计与经验协方差估计 (../examples/covariance/plot_robust_vs_empirical_covariance.py) |
00:02.433 |
0.0 |
可视化股票市场结构 (../examples/applications/plot_stock_market.py) |
00:02.416 |
0.0 |
使用部分依赖进行高级绘图 (../examples/miscellaneous/plot_partial_dependence_visualization_api.py) |
00:02.362 |
0.0 |
Ledoit-Wolf与OAS估计的比较 (../examples/covariance/plot_lw_vs_oas.py) |
00:02.298 |
0.0 |
概率校准曲线 (../examples/calibration/plot_calibration_curve.py) |
00:02.297 |
0.0 |
scikit-learn 1.4 发布亮点 (../examples/release_highlights/plot_release_highlights_1_4_0.py) |
00:02.225 |
0.0 |
人脸部分字典的在线学习 (../examples/cluster/plot_dict_face_patches.py) |
00:02.129 |
0.0 |
机器学习无法推断因果效应 (../examples/inspection/plot_causal_interpretation.py) |
00:02.088 |
0.0 |
矢量量化示例 (../examples/cluster/plot_face_compress.py) |
00:02.082 |
0.0 |
分类器比较 (../examples/classification/plot_classifier_comparison.py) |
00:02.062 |
0.0 |
鸢尾花数据集上的主成分分析（PCA） (../examples/decomposition/plot_pca_iris.py) |
00:02.021 |
0.0 |
使用高斯过程分类（GPC）进行概率预测 (../examples/gaussian_process/plot_gpc.py) |
00:01.990 |
0.0 |
归纳聚类 (../examples/cluster/plot_inductive_clustering.py) |
00:01.957 |
0.0 |
在玩具数据集上比较不同的层次链接方法 (../examples/cluster/plot_linkage_comparison.py) |
00:01.926 |
0.0 |
将数据映射到正态分布 (../examples/preprocessing/plot_map_data_to_normal.py) |
00:01.859 |
0.0 |
有结构和无结构的凝聚聚类 (../examples/cluster/plot_agglomerative_clustering.py) |
00:01.845 |
0.0 |
多层感知机中正则化的变化 (../examples/neural_networks/plot_mlp_alpha.py) |
00:01.812 |
0.0 |
使用分类器链进行多标签分类 (../examples/multioutput/plot_classifier_chain_yeast.py) |
00:01.779 |
0.0 |
稳健线性估计器拟合 (../examples/linear_model/plot_robust_fit.py) |
00:01.766 |
0.0 |
使用类别似然比衡量分类性能 (../examples/model_selection/plot_likelihood_ratios.py) |
00:01.741 |
0.0 |
使用邻域成分分析进行降维 (../examples/neighbors/plot_nca_dim_reduction.py) |
00:01.621 |
0.0 |
特征缩放的重要性 (../examples/preprocessing/plot_scaling_importance.py) |
00:01.607 |
0.0 |
使用多输出估计器进行人脸补全 (../examples/miscellaneous/plot_multioutput_face_completion.py) |
00:01.604 |
0.0 |
RBF核的显式特征映射近似 (../examples/miscellaneous/plot_kernel_approximation.py) |
00:01.582 |
0.0 |
scikit-learn 1.3 发布亮点 (../examples/release_highlights/plot_release_highlights_1_3_0.py) |
00:01.577 |
0.0 |
使用网格搜索进行模型的统计比较 (../examples/model_selection/plot_grid_search_stats.py) |
00:01.514 |
0.0 |
不同核函数的先验和后验高斯过程图示 (../examples/gaussian_process/plot_gpr_prior_posterior.py) |
00:01.490 |
0.0 |
OPTICS聚类算法演示 (../examples/cluster/plot_optics.py) |
00:01.474 |
0.0 |
scikit-learn 0.22 发布亮点 (../examples/release_highlights/plot_release_highlights_0_22_0.py) |
00:01.450 |
0.0 |
压缩感知：使用L1先验（Lasso）进行断层扫描重建 (../examples/applications/plot_tomography_l1_reconstruction.py) |
00:01.421 |
0.0 |
高斯混合模型选择 (../examples/mixture/plot_gmm_selection.py) |
00:01.407 |
0.0 |
数字2D嵌入上的各种凝聚聚类 (../examples/cluster/plot_digits_linkage.py) |
00:01.402 |
0.0 |
使用不同SVM核绘制分类边界 (../examples/svm/plot_svm_kernels.py) |
00:01.350 |
0.0 |
3类分类的概率校准 (../examples/calibration/plot_calibration_multiclass.py) |
00:01.338 |
0.0 |
回归模型中目标变换的影响 (../examples/compose/plot_transformed_target.py) |
00:01.317 |
0.0 |
梯度提升回归 (../examples/ensemble/plot_gradient_boosting_regression.py) |
00:01.309 |
0.0 |
密集和稀疏数据上的Lasso (../examples/linear_model/plot_lasso_dense_vs_sparse_data.py) |
00:01.288 |
0.0 |
k-means初始化影响的经验评估 (../examples/cluster/plot_kmeans_stability_low_dim_dense.py) |
00:01.253 |
0.0 |
最近邻缓存 (../examples/neighbors/plot_caching_nearest_neighbors.py) |
00:01.200 |
0.0 |
混合类型的列转换器 (../examples/compose/plot_column_transformer_mixed_types.py) |
00:01.172 |
0.0 |
可视化scikit-learn中的交叉验证行为 (../examples/model_selection/plot_cv_indices.py) |
00:01.158 |
0.0 |
单一估计器与 bagging：偏差-方差分解 (../examples/ensemble/plot_bias_variance.py) |
00:01.151 |
0.0 |
手写数字数据上的K-Means聚类演示 (../examples/cluster/plot_kmeans_digits.py) |
00:01.123 |
0.0 |
k-means假设的演示 (../examples/cluster/plot_kmeans_assumptions.py) |
00:01.071 |
0.0 |
使用轮廓分析在KMeans聚类中选择聚类数量 (../examples/cluster/plot_kmeans_silhouette_analysis.py) |
00:01.067 |
0.0 |
使用不同度量的凝聚聚类 (../examples/cluster/plot_agglomerative_clustering_metrics.py) |
00:01.062 |
0.0 |
聚类性能评估中的偶然性调整 (../examples/cluster/plot_adjusted_for_chance_measures.py) |
00:01.057 |
0.0 |
二分K-Means和常规K-Means性能比较 (../examples/cluster/plot_bisect_kmeans.py) |
00:01.051 |
0.0 |
SVM平局处理示例 (../examples/svm/plot_svm_tie_breaking.py) |
00:01.020 |
0.0 |
管道：链式连接PCA和逻辑回归 (../examples/compose/plot_digits_pipe.py) |
00:01.014 |
0.0 |
scikit-learn 1.1 发布亮点 (../examples/release_highlights/plot_release_highlights_1_1_0.py) |
00:00.983 |
0.0 |
绘制个体和投票回归预测 (../examples/ensemble/plot_voting_regressor.py) |
00:00.968 |
0.0 |
Lasso模型选择：AIC-BIC / 交叉验证 (../examples/linear_model/plot_lasso_model_selection.py) |
00:00.901 |
0.0 |
使用树森林的特征重要性 (../examples/ensemble/plot_forest_importances.py) |
00:00.899 |
0.0 |
Lasso、Lasso-LARS和Elastic Net路径 (../examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.py) |
00:00.886 |
0.0 |
分位数回归 (../examples/linear_model/plot_quantile_regression.py) |
00:00.870 |
0.0 |
鸢尾花数据集上半监督分类器与SVM的决策边界 (../examples/semi_supervised/plot_semi_supervised_versus_svm_iris.py) |
00:00.856 |
0.0 |
scikit-learn 1.5 发布亮点 (../examples/release_highlights/plot_release_highlights_1_5_0.py) |
00:00.781 |
0.0 |
绘制在鸢尾花数据集上训练的决策树决策面 (../examples/tree/plot_iris_dtc.py) |
00:00.774 |
0.0 |
使用预计算Gram矩阵和加权样本拟合Elastic Net (../examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.py) |
00:00.770 |
0.0 |
带交叉验证的递归特征消除 (../examples/feature_selection/plot_rfe_with_cross_validation.py) |
00:00.768 |
0.0 |
使用局部异常因子（LOF）进行新颖性检测 (../examples/neighbors/plot_lof_novelty_detection.py) |
00:00.728 |
0.0 |
比较带和不带邻域成分分析的最近邻 (../examples/neighbors/plot_nca_classification.py) |
00:00.723 |
0.0 |
两类AdaBoost (../examples/ensemble/plot_adaboost_twoclass.py) |
00:00.669 |
0.0 |
可视化投票分类器的概率预测 (../examples/ensemble/plot_voting_decision_regions.py) |
00:00.660 |
0.0 |
GMM初始化方法 (../examples/mixture/plot_gmm_init.py) |
00:00.651 |
0.0 |
岭回归系数随L2正则化的变化 (../examples/linear_model/plot_ridge_coeffs.py) |
00:00.645 |
0.0 |
比较线性贝叶斯回归器 (../examples/linear_model/plot_ard.py) |
00:00.640 |
0.0 |
多项式和一对多逻辑回归的决策边界 (../examples/linear_model/plot_logistic_multinomial.py) |
00:00.624 |
0.0 |
scikit-learn 0.23 发布亮点 (../examples/release_highlights/plot_release_highlights_0_23_0.py) |
00:00.621 |
0.0 |
演示KBinsDiscretizer的不同策略 (../examples/preprocessing/plot_discretization_strategies.py) |
00:00.599 |
0.0 |
多类受试者工作特征曲线（ROC） (../examples/model_selection/plot_roc.py) |
00:00.599 |
0.0 |
单调约束 (../examples/ensemble/plot_monotonic_constraints.py) |
00:00.587 |
0.0 |
核主成分分析 (../examples/decomposition/plot_kernel_pca.py) |
00:00.548 |
0.0 |
谱双向聚类算法演示 (../examples/bicluster/plot_spectral_biclustering.py) |
00:00.525 |
0.0 |
简单一维核密度估计 (../examples/neighbors/plot_kde_1d.py) |
00:00.522 |
0.0 |
Theil-Sen回归 (../examples/linear_model/plot_theilsen.py) |
00:00.518 |
0.0 |
比较随机森林和多输出元估计器 (../examples/ensemble/plot_random_forest_regression_multioutput.py) |
00:00.511 |
0.0 |
最近邻分类 (../examples/neighbors/plot_classification.py) |
00:00.498 |
0.0 |
高斯过程回归：基本入门示例 (../examples/gaussian_process/plot_gpr_noisy_targets.py) |
00:00.486 |
0.0 |
主成分回归与偏最小二乘回归比较 (../examples/cross_decomposition/plot_pcr_vs_pls.py) |
00:00.483 |
0.0 |
IsolationForest示例 (../examples/ensemble/plot_isolation_forest.py) |
00:00.478 |
0.0 |
特征聚合与单变量选择 (../examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py) |
00:00.462 |
0.0 |
标签传播数字：主动学习 (../examples/semi_supervised/plot_label_propagation_digits_active_learning.py) |
00:00.460 |
0.0 |
稀疏信号的L1模型 (../examples/linear_model/plot_lasso_and_elasticnet.py) |
00:00.446 |
0.0 |
逻辑回归中的L1惩罚和稀疏性 (../examples/linear_model/plot_logistic_l1_l2_sparsity.py) |
00:00.441 |
0.0 |
带AdaBoost的决策树回归 (../examples/ensemble/plot_adaboost_regression.py) |
00:00.433 |
0.0 |
XOR数据集上的高斯过程分类（GPC）图示 (../examples/gaussian_process/plot_gpc_xor.py) |
00:00.432 |
0.0 |
高斯混合模型正弦曲线 (../examples/mixture/plot_gmm_sin.py) |
00:00.425 |
0.0 |
用于图像分割的谱聚类 (../examples/cluster/plot_segmentation_toy.py) |
00:00.424 |
0.0 |
多项式和样条插值 (../examples/linear_model/plot_polynomial_interpolation.py) |
00:00.423 |
0.0 |
使用成本复杂性剪枝对决策树进行后剪枝 (../examples/tree/plot_cost_complexity_pruning.py) |
00:00.419 |
0.0 |
稀疏逆协方差估计 (../examples/covariance/plot_sparse_cov.py) |
00:00.419 |
0.0 |
一类SVM与使用随机梯度下降的一类SVM比较 (../examples/linear_model/plot_sgdocsvm_vs_ocsvm.py) |
00:00.418 |
0.0 |
手写数字识别 (../examples/classification/plot_digits_classification.py) |
00:00.413 |
0.0 |
普通最小二乘和岭回归 (../examples/linear_model/plot_ols_ridge.py) |
00:00.411 |
0.0 |
连接多种特征提取方法 (../examples/compose/plot_feature_union.py) |
00:00.406 |
0.0 |
均值漂移聚类算法演示 (../examples/cluster/plot_mean_shift.py) |
00:00.403 |
0.0 |
收缩协方差估计：LedoitWolf与OAS和最大似然比较 (../examples/covariance/plot_covariance_estimation.py) |
00:00.400 |
0.0 |
带协方差椭圆的线性和二次判别分析 (../examples/classification/plot_lda_qda.py) |
00:00.395 |
0.0 |
真实数据集上的异常点检测 (../examples/applications/plot_outlier_detection_wine.py) |
00:00.395 |
0.0 |
层次聚类：结构化与非结构化Ward (../examples/cluster/plot_ward_structured_vs_unstructured.py) |
00:00.378 |
0.0 |
因子分析（带旋转）以可视化模式 (../examples/decomposition/plot_varimax_fa.py) |
00:00.375 |
0.0 |
精确率-召回率 (../examples/model_selection/plot_precision_recall.py) |
00:00.364 |
0.0 |
2D点云上的FastICA (../examples/decomposition/plot_ica_vs_pca.py) |
00:00.355 |
0.0 |
分类器的概率校准 (../examples/calibration/plot_calibration.py) |
00:00.351 |
0.0 |
使用完全随机树进行哈希特征变换 (../examples/ensemble/plot_random_forest_embedding.py) |
00:00.349 |
0.0 |
硬币图像上的结构化Ward层次聚类演示 (../examples/cluster/plot_coin_ward_segmentation.py) |
00:00.345 |
0.0 |
使用FastICA进行盲源分离 (../examples/decomposition/plot_ica_blind_source_separation.py) |
00:00.341 |
0.0 |
谱协同聚类算法演示 (../examples/bicluster/plot_spectral_coclustering.py) |
00:00.336 |
0.0 |
绘制岭回归系数随正则化的变化 (../examples/linear_model/plot_ridge_path.py) |
00:00.329 |
0.0 |
SVM-Anova：带单变量特征选择的SVM (../examples/svm/plot_svm_anova.py) |
00:00.313 |
0.0 |
使用显示对象进行可视化 (../examples/miscellaneous/plot_display_object_visualization.py) |
00:00.307 |
0.0 |
标签传播数字：性能演示 (../examples/semi_supervised/plot_label_propagation_digits.py) |
00:00.295 |
0.0 |
决策树回归 (../examples/tree/plot_tree_regression.py) |
00:00.291 |
0.0 |
目标编码器的内部交叉拟合 (../examples/preprocessing/plot_target_encoder_cross_val.py) |
00:00.285 |
0.0 |
亲和传播聚类算法演示 (../examples/cluster/plot_affinity_propagation.py) |
00:00.285 |
0.0 |
使用贝叶斯岭回归进行曲线拟合 (../examples/linear_model/plot_bayesian_ridge_curvefit.py) |
00:00.280 |
0.0 |
SVM：加权样本 (../examples/svm/plot_weighted_samples.py) |
00:00.271 |
0.0 |
稳健协方差估计和马氏距离相关性 (../examples/covariance/plot_mahalanobis_distances.py) |
00:00.259 |
0.0 |
使用预计算字典进行稀疏编码 (../examples/decomposition/plot_sparse_coding.py) |
00:00.254 |
0.0 |
SGD：惩罚项 (../examples/linear_model/plot_sgd_penalties.py) |
00:00.250 |
0.0 |
多维缩放 (../examples/manifold/plot_mds.py) |
00:00.236 |
0.0 |
使用多任务Lasso进行联合特征选择 (../examples/linear_model/plot_multi_task_lasso_support.py) |
00:00.228 |
0.0 |
增量PCA (../examples/decomposition/plot_incremental_pca.py) |
00:00.224 |
0.0 |
F检验和互信息的比较 (../examples/feature_selection/plot_f_test_vs_mi.py) |
00:00.223 |
0.0 |
单变量特征选择 (../examples/feature_selection/plot_feature_selection.py) |
00:00.221 |
0.0 |
离散数据结构上的高斯过程 (../examples/gaussian_process/plot_gpr_on_structured_data.py) |
00:00.218 |
0.0 |
使用 KBinsDiscretizer 离散化连续特征 (../examples/preprocessing/plot_discretization.py) |
00:00.209 |
0.0 |
高斯混合模型椭球体 (../examples/mixture/plot_gmm.py) |
00:00.208 |
0.0 |
scikit-learn 1.7 发布亮点 (../examples/release_highlights/plot_release_highlights_1_7_0.py) |
00:00.206 |
0.0 |
绘制 LinearSVC 中的支持向量 (../examples/svm/plot_linearsvc_support_vectors.py) |
00:00.204 |
0.0 |
在鸢尾花数据集上绘制不同的 SVM 分类器 (../examples/svm/plot_iris_svc.py) |
00:00.201 |
0.0 |
最近邻回归 (../examples/neighbors/plot_regression.py) |
00:00.200 |
0.0 |
GMM 协方差 (../examples/mixture/plot_gmm_covariances.py) |
00:00.195 |
0.0 |
正交匹配追踪 (../examples/linear_model/plot_omp.py) |
00:00.195 |
0.0 |
检测误差权衡 (DET) 曲线 (../examples/model_selection/plot_det.py) |
00:00.188 |
0.0 |
欠拟合与过拟合 (../examples/model_selection/plot_underfitting_overfitting.py) |
00:00.188 |
0.0 |
绘制交叉验证预测结果 (../examples/model_selection/plot_cv_predict.py) |
00:00.183 |
0.0 |
鸢尾花数据集的 LDA 和 PCA 2D 投影比较 (../examples/decomposition/plot_pca_vs_lda.py) |
00:00.183 |
0.0 |
比较交叉分解方法 (../examples/cross_decomposition/plot_compare_cross_decomposition.py) |
00:00.181 |
0.0 |
带交叉验证的接收者操作特征 (ROC) 曲线 (../examples/model_selection/plot_roc_crossval.py) |
00:00.171 |
0.0 |
多标签分类 (../examples/miscellaneous/plot_multilabel.py) |
00:00.169 |
0.0 |
L1 逻辑回归的正则化路径 (../examples/linear_model/plot_logistic_path.py) |
00:00.164 |
0.0 |
DBSCAN 聚类算法演示 (../examples/cluster/plot_dbscan.py) |
00:00.163 |
0.0 |
K-Means 与 MiniBatchKMeans 聚类算法比较 (../examples/cluster/plot_mini_batch_kmeans.py) |
00:00.163 |
0.0 |
混淆矩阵 (../examples/model_selection/plot_confusion_matrix.py) |
00:00.161 |
0.0 |
SVM：非平衡类别的分离超平面 (../examples/svm/plot_separating_hyperplane_unbalanced.py) |
00:00.157 |
0.0 |
带非线性核 (RBF) 的单分类 SVM (../examples/svm/plot_oneclass.py) |
00:00.153 |
0.0 |
最近质心分类 (../examples/neighbors/plot_nearest_centroid.py) |
00:00.152 |
0.0 |
邻域成分分析示意图 (../examples/neighbors/plot_nca_illustration.py) |
00:00.150 |
0.0 |
使用可视化 API 的 ROC 曲线 (../examples/miscellaneous/plot_roc_curve_visualization_api.py) |
00:00.147 |
0.0 |
标签传播圈：学习复杂结构 (../examples/semi_supervised/plot_label_propagation_structure.py) |
00:00.145 |
0.0 |
介绍 set_output API (../examples/miscellaneous/plot_set_output.py) |
00:00.142 |
0.0 |
等渗回归 (../examples/miscellaneous/plot_isotonic_regression.py) |
00:00.136 |
0.0 |
高斯过程分类 (GPC) 的等概率线 (../examples/gaussian_process/plot_gpc_isoprobability.py) |
00:00.130 |
0.0 |
显示管道 (../examples/miscellaneous/plot_pipeline_display.py) |
00:00.128 |
0.0 |
绘制随机生成的多标签数据集 (../examples/datasets/plot_random_multilabel_dataset.py) |
00:00.124 |
0.0 |
高斯混合的密度估计 (../examples/mixture/plot_gmm_pdf.py) |
00:00.121 |
0.0 |
逻辑函数 (../examples/linear_model/plot_logistic.py) |
00:00.120 |
0.0 |
特征聚合 (../examples/cluster/plot_digits_agglomeration.py) |
00:00.117 |
0.0 |
在鸢尾花数据集上绘制多类别 SGD (../examples/linear_model/plot_sgd_iris.py) |
00:00.112 |
0.0 |
HuberRegressor 与 Ridge 在强异常值数据集上的比较 (../examples/linear_model/plot_huber_vs_ridge.py) |
00:00.112 |
0.0 |
scikit-learn 1.6 发布亮点 (../examples/release_highlights/plot_release_highlights_1_6_0.py) |
00:00.104 |
0.0 |
通过信息准则进行 Lasso 模型选择 (../examples/linear_model/plot_lasso_lars_ic.py) |
00:00.097 |
0.0 |
SGD：凸损失函数 (../examples/linear_model/plot_sgd_loss_functions.py) |
00:00.092 |
0.0 |
使用 RANSAC 进行鲁棒线性模型估计 (../examples/linear_model/plot_ransac.py) |
00:00.090 |
0.0 |
带自定义核的 SVM (../examples/svm/plot_custom_kernel.py) |
00:00.090 |
0.0 |
绘制层次聚类树状图 (../examples/cluster/plot_agglomerative_dendrogram.py) |
00:00.083 |
0.0 |
使用局部异常因子 (LOF) 进行异常值检测 (../examples/neighbors/plot_lof_outlier_detection.py) |
00:00.077 |
0.0 |
理解决策树结构 (../examples/tree/plot_unveil_tree_structure.py) |
00:00.076 |
0.0 |
SGD：最大间隔分离超平面 (../examples/linear_model/plot_sgd_separating_hyperplane.py) |
00:00.069 |
0.0 |
SGD：加权样本 (../examples/linear_model/plot_sgd_weighted_samples.py) |
00:00.068 |
0.0 |
SVM：最大间隔分离超平面 (../examples/svm/plot_separating_hyperplane.py) |
00:00.065 |
0.0 |
非负最小二乘 (../examples/linear_model/plot_nnls.py) |
00:00.064 |
0.0 |
SVM 间隔示例 (../examples/svm/plot_svm_margin.py) |
00:00.062 |
0.0 |
K-Means++ 初始化示例 (../examples/cluster/plot_kmeans_plusplus.py) |
00:00.057 |
0.0 |
元数据路由 (../examples/miscellaneous/plot_metadata_routing.py) |
00:00.044 |
0.0 |
显示估计器和复杂管道 (../examples/miscellaneous/plot_estimator_representation.py) |
00:00.030 |
0.0 |
FrozenEstimator 使用示例 (../examples/frozen/plot_frozen_examples.py) |
00:00.023 |
0.0 |
scikit-learn 1.0 发布亮点 (../examples/release_highlights/plot_release_highlights_1_0_0.py) |
00:00.015 |
0.0 |
管道 ANOVA SVM (../examples/feature_selection/plot_feature_selection_pipeline.py) |
00:00.014 |
0.0 |
维基百科主特征向量 (../examples/applications/wikipedia_principal_eigenvector.py) |
00:00.000 |
0.0 |
__sklearn_is_fitted__ 作为开发者 API (../examples/developing_estimators/sklearn_is_fitted.py) |
00:00.000 |
0.0 |
TSNE 中的近似最近邻 (../examples/neighbors/approximate_nearest_neighbors.py) |
00:00.000 |
0.0 |
