异常值检测估计器的评估 (../examples/miscellaneous/plot_outlier_detection_bench.py) |
01:05.097 |
0.0 |
基于模型的特征选择和顺序特征选择 (../examples/feature_selection/plot_select_from_model_diabetes.py) |
00:50.848 |
0.0 |
使用Pipeline和GridSearchCV选择降维方法 (../examples/compose/plot_compare_reduction.py) |
00:42.909 |
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) |
00:39.620 |
0.0 |
使用多项式核近似进行可扩展学习 (../examples/kernel_approximation/plot_scalable_poly_kernels.py) |
00:39.458 |
0.0 |
手写数字的流形学习：局部线性嵌入、Isomap等 (../examples/manifold/plot_lle_digits.py) |
00:37.999 |
0.0 |
成本敏感学习的决策阈值后调优 (../examples/model_selection/plot_cost_sensitive_learning.py) |
00:35.875 |
0.0 |
决策函数截止点的后验调优 (../examples/model_selection/plot_tuned_decision_threshold.py) |
00:33.969 |
0.0 |
绘制学习曲线并检查模型的可扩展性 (../examples/model_selection/plot_learning_curve.py) |
00:29.969 |
0.0 |
使用字典学习进行图像去噪 (../examples/decomposition/plot_image_denoising.py) |
00:29.622 |
0.0 |
随机梯度下降的早期停止 (../examples/linear_model/plot_sgd_early_stopping.py) |
00:28.933 |
0.0 |
比较超参数估计的随机搜索和网格搜索 (../examples/model_selection/plot_randomized_search.py) |
00:28.031 |
0.0 |
文本特征提取和评估的示例管道 (../examples/model_selection/plot_grid_search_text_feature_extraction.py) |
00:26.612 |
0.0 |
比较目标编码器与其他编码器 (../examples/preprocessing/plot_target_encoder.py) |
00:21.022 |
0.0 |
流形学习方法比较 (../examples/manifold/plot_compare_methods.py) |
00:20.244 |
0.0 |
局部依赖和个体条件期望图 (../examples/inspection/plot_partial_dependence.py) |
00:20.237 |
0.0 |
使用堆叠组合预测器 (../examples/ensemble/plot_stack_predictors.py) |
00:19.441 |
0.0 |
切断球体上的流形学习方法 (../examples/manifold/plot_manifold_sphere.py) |
00:19.112 |
0.0 |
平衡模型复杂度和交叉验证分数 (../examples/model_selection/plot_grid_search_refit_callable.py) |
00:18.332 |
0.0 |
直方图梯度提升树中的特征 (../examples/ensemble/plot_hgbt_regression.py) |
00:18.194 |
0.0 |
泊松回归和非正态损失 (../examples/linear_model/plot_poisson_regression_non_normal_loss.py) |
00:17.753 |
0.0 |
多类别训练元估计器概述 (../examples/multiclass/plot_multiclass_overview.py) |
00:17.156 |
0.0 |
瑞士卷和瑞士孔降维 (../examples/manifold/plot_swissroll.py) |
00:16.974 |
0.0 |
SVC的正则化参数缩放 (../examples/svm/plot_svm_scale_c.py) |
00:15.820 |
0.0 |
时间相关特征工程 (../examples/applications/plot_cyclical_feature_engineering.py) |
00:15.109 |
0.0 |
scikit-learn 0.24版本亮点 (../examples/release_highlights/plot_release_highlights_0_24_0.py) |
00:14.909 |
0.0 |
核岭回归和SVR的比较 (../examples/miscellaneous/plot_kernel_ridge_regression.py) |
00:14.356 |
0.0 |
scikit-learn 1.8版本亮点 (../examples/release_highlights/plot_release_highlights_1_8_0.py) |
00:13.717 |
0.0 |
预测延迟 (../examples/applications/plot_prediction_latency.py) |
00:13.003 |
0.0 |
随机投影嵌入的Johnson-Lindenstrauss界限 (../examples/miscellaneous/plot_johnson_lindenstrauss_bound.py) |
00:12.171 |
0.0 |
HDBSCAN聚类算法演示 (../examples/cluster/plot_hdbscan.py) |
00:11.854 |
0.0 |
使用排列检验分类分数的显著性 (../examples/model_selection/plot_permutation_tests_for_classification.py) |
00:11.151 |
0.0 |
使用多项式逻辑回归 + L1在MNIST上进行分类 (../examples/linear_model/plot_sparse_logistic_regression_mnist.py) |
00:10.791 |
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) |
00:10.200 |
0.0 |
线性模型系数解释中的常见陷阱 (../examples/inspection/plot_linear_model_coefficient_interpretation.py) |
00:09.893 |
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) |
00:09.887 |
0.0 |
梯度提升回归的预测区间 (../examples/ensemble/plot_gradient_boosting_quantile.py) |
00:09.880 |
0.0 |
在cross_val_score和GridSearchCV上演示多指标评估 (../examples/model_selection/plot_multi_metric_evaluation.py) |
00:09.662 |
0.0 |
梯度提升的袋外估计 (../examples/ensemble/plot_gradient_boosting_oob.py) |
00:09.347 |
0.0 |
保险索赔上的Tweedie回归 (../examples/linear_model/plot_tweedie_regression_insurance_claims.py) |
00:09.220 |
0.0 |
鸢尾花数据集上的高斯过程分类 (GPC) (../examples/gaussian_process/plot_gpc_iris.py) |
00:08.552 |
0.0 |
时间序列预测的滞后特征 (../examples/applications/plot_time_series_lagged_features.py) |
00:08.489 |
0.0 |
在构建估计器之前填充缺失值 (../examples/impute/plot_missing_values.py) |
00:08.208 |
0.0 |
人脸数据集分解 (../examples/decomposition/plot_faces_decomposition.py) |
00:08.159 |
0.0 |
用于分类的Normal、Ledoit-Wolf和OAS线性判别分析 (../examples/classification/plot_lda.py) |
00:08.019 |
0.0 |
物种分布建模 (../examples/applications/plot_species_distribution_modeling.py) |
00:07.403 |
0.0 |
比较不同缩放器对带异常值数据的影响 (../examples/preprocessing/plot_all_scaling.py) |
00:07.123 |
0.0 |
网格搜索与连续减半的比较 (../examples/model_selection/plot_successive_halving_heatmap.py) |
00:07.082 |
0.0 |
具有多重共线或相关特征的排列重要性 (../examples/inspection/plot_permutation_importance_multicollinear.py) |
00:07.050 |
0.0 |
梯度提升正则化 (../examples/ensemble/plot_gradient_boosting_regularization.py) |
00:06.976 |
0.0 |
文本文档的核外分类 (../examples/applications/plot_out_of_core_classification.py) |
00:06.937 |
0.0 |
使用k-means对文本文档进行聚类 (../examples/text/plot_document_clustering.py) |
00:06.559 |
0.0 |
嵌套与非嵌套交叉验证 (../examples/model_selection/plot_nested_cross_validation_iris.py) |
00:06.443 |
0.0 |
变分贝叶斯高斯混合的浓度先验类型分析 (../examples/mixture/plot_concentration_prior.py) |
00:06.295 |
0.0 |
排列重要性 vs 随机森林特征重要性 (MDI) (../examples/inspection/plot_permutation_importance.py) |
00:06.265 |
0.0 |
使用IterativeImputer的变体填充缺失值 (../examples/impute/plot_iterative_imputer_variants_comparison.py) |
00:06.116 |
0.0 |
绘制鸢尾花数据集上树集成模型的决策面 (../examples/ensemble/plot_forest_iris.py) |
00:06.066 |
0.0 |
使用稀疏特征对文本文档进行分类 (../examples/text/plot_document_classification_20newsgroups.py) |
00:06.043 |
0.0 |
使用特征脸和SVM进行人脸识别示例 (../examples/applications/plot_face_recognition.py) |
00:06.002 |
0.0 |
使用核PCA进行图像去噪 (../examples/applications/plot_digits_denoising.py) |
00:05.967 |
0.0 |
使用谱系双聚类算法对文档进行双聚类 (../examples/bicluster/plot_bicluster_newsgroups.py) |
00:05.701 |
0.0 |
scikit-learn 1.2版本亮点 (../examples/release_highlights/plot_release_highlights_1_2_0.py) |
00:05.676 |
0.0 |
在玩具数据集上比较不同的聚类算法 (../examples/cluster/plot_cluster_comparison.py) |
00:05.658 |
0.0 |
使用线性和非线性核的支持向量回归 (SVR) (../examples/svm/plot_svm_regression.py) |
00:05.648 |
0.0 |
20newsgroups上的多类别稀疏逻辑回归 (../examples/linear_model/plot_sparse_logistic_regression_20newsgroups.py) |
00:05.612 |
0.0 |
连续减半迭代 (../examples/model_selection/plot_successive_halving_iterations.py) |
00:05.524 |
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) |
00:05.486 |
0.0 |
MNIST上MLP权重的可视化 (../examples/neural_networks/plot_mnist_filters.py) |
00:05.423 |
0.0 |
自训练阈值变化的影响 (../examples/semi_supervised/plot_self_training_varying_threshold.py) |
00:05.289 |
0.0 |
可视化股票市场结构 (../examples/applications/plot_stock_market.py) |
00:04.902 |
0.0 |
对希腊硬币图片进行区域分割 (../examples/cluster/plot_coin_segmentation.py) |
00:04.819 |
0.0 |
RBF SVM参数 (../examples/svm/plot_rbf_parameters.py) |
00:04.772 |
0.0 |
文本数据集上的半监督分类 (../examples/semi_supervised/plot_semi_supervised_newsgroups.py) |
00:04.771 |
0.0 |
模型正则化对训练和测试误差的影响 (../examples/model_selection/plot_train_error_vs_test_error.py) |
00:04.646 |
0.0 |
FeatureHasher和DictVectorizer比较 (../examples/text/plot_hashing_vs_dict_vectorizer.py) |
00:04.607 |
0.0 |
模型复杂度的影响 (../examples/applications/plot_model_complexity_influence.py) |
00:04.435 |
0.0 |
梯度提升中的分类特征支持 (../examples/ensemble/plot_gradient_boosting_categorical.py) |
00:04.379 |
0.0 |
手写数字数据上的K-Means聚类演示 (../examples/cluster/plot_kmeans_digits.py) |
00:04.309 |
0.0 |
核岭回归和高斯过程回归的比较 (../examples/gaussian_process/plot_compare_gpr_krr.py) |
00:04.117 |
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) |
00:03.994 |
0.0 |
核密度估计 (../examples/neighbors/plot_digits_kde_sampling.py) |
00:03.712 |
0.0 |
特征离散化 (../examples/preprocessing/plot_discretization_classification.py) |
00:03.436 |
0.0 |
物种分布的核密度估计 (../examples/neighbors/plot_species_kde.py) |
00:03.413 |
0.0 |
多类别AdaBoost决策树 (../examples/ensemble/plot_adaboost_multiclass.py) |
00:03.299 |
0.0 |
在玩具数据集上比较异常值检测算法 (../examples/miscellaneous/plot_anomaly_comparison.py) |
00:03.269 |
0.0 |
随机森林的OOB误差 (../examples/ensemble/plot_ensemble_oob.py) |
00:03.264 |
0.0 |
比较BIRCH和MiniBatchKMeans (../examples/cluster/plot_birch_vs_minibatchkmeans.py) |
00:03.141 |
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) |
00:03.134 |
0.0 |
t-SNE：不同困惑度值对形状的影响 (../examples/manifold/plot_t_sne_perplexity.py) |
00:02.924 |
0.0 |
递归特征消除 (../examples/feature_selection/plot_rfe_digits.py) |
00:02.804 |
0.0 |
分类器校准比较 (../examples/calibration/plot_compare_calibration.py) |
00:02.760 |
0.0 |
比较MLPClassifier的随机学习策略 (../examples/neural_networks/plot_mlp_training_curves.py) |
00:02.553 |
0.0 |
使用预计算的Gram矩阵和加权样本拟合弹性网络 (../examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.py) |
00:02.551 |
0.0 |
稳健协方差与经验协方差估计 (../examples/covariance/plot_robust_vs_empirical_covariance.py) |
00:02.515 |
0.0 |
梯度提升中的早期停止 (../examples/ensemble/plot_gradient_boosting_early_stopping.py) |
00:02.422 |
0.0 |
具有异构数据源的列转换器 (../examples/compose/plot_column_transformer.py) |
00:02.417 |
0.0 |
使用多输出估计器进行人脸补全 (../examples/miscellaneous/plot_multioutput_face_completion.py) |
00:02.411 |
0.0 |
绘制分类概率 (../examples/classification/plot_classification_probability.py) |
00:02.401 |
0.0 |
概率校准曲线 (../examples/calibration/plot_calibration_curve.py) |
00:02.376 |
0.0 |
使用局部依赖的高级绘图 (../examples/miscellaneous/plot_partial_dependence_visualization_api.py) |
00:02.296 |
0.0 |
Ledoit-Wolf vs OAS估计 (../examples/covariance/plot_lw_vs_oas.py) |
00:02.252 |
0.0 |
用于数字分类的受限玻尔兹曼机特征 (../examples/neural_networks/plot_rbm_logistic_classification.py) |
00:02.139 |
0.0 |
精确度-召回率 (../examples/model_selection/plot_precision_recall.py) |
00:02.113 |
0.0 |
使用树集成进行特征转换 (../examples/ensemble/plot_feature_transformation.py) |
00:02.026 |
0.0 |
有结构和无结构的层次聚类 (../examples/cluster/plot_ward_structured_vs_unstructured.py) |
00:01.992 |
0.0 |
高斯过程分类 (GPC) 的概率预测 (../examples/gaussian_process/plot_gpc.py) |
00:01.986 |
0.0 |
归纳聚类 (../examples/cluster/plot_inductive_clustering.py) |
00:01.972 |
0.0 |
鸢尾花数据集上的主成分分析 (PCA) (../examples/decomposition/plot_pca_iris.py) |
00:01.923 |
0.0 |
将数据映射到正态分布 (../examples/preprocessing/plot_map_data_to_normal.py) |
00:01.893 |
0.0 |
稳健线性估计器拟合 (../examples/linear_model/plot_robust_fit.py) |
00:01.891 |
0.0 |
scikit-learn 1.4版本亮点 (../examples/release_highlights/plot_release_highlights_1_4_0.py) |
00:01.890 |
0.0 |
分类器比较 (../examples/classification/plot_classifier_comparison.py) |
00:01.879 |
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) |
00:01.770 |
0.0 |
向量量化示例 (../examples/cluster/plot_face_compress.py) |
00:01.753 |
0.0 |
使用类别似然比衡量分类性能 (../examples/model_selection/plot_likelihood_ratios.py) |
00:01.750 |
0.0 |
人脸补丁字典的在线学习 (../examples/cluster/plot_dict_face_patches.py) |
00:01.721 |
0.0 |
机器学习无法推断因果效应 (../examples/inspection/plot_causal_interpretation.py) |
00:01.693 |
0.0 |
多层感知器中的不同正则化 (../examples/neural_networks/plot_mlp_alpha.py) |
00:01.663 |
0.0 |
scikit-learn 0.22版本亮点 (../examples/release_highlights/plot_release_highlights_0_22_0.py) |
00:01.661 |
0.0 |
使用分类器链进行多标签分类 (../examples/multioutput/plot_classifier_chain_yeast.py) |
00:01.655 |
0.0 |
RBF核的显式特征图近似 (../examples/miscellaneous/plot_kernel_approximation.py) |
00:01.631 |
0.0 |
数字的2D嵌入上的各种凝聚聚类 (../examples/cluster/plot_digits_linkage.py) |
00:01.543 |
0.0 |
使用邻域成分分析进行降维 (../examples/neighbors/plot_nca_dim_reduction.py) |
00:01.485 |
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) |
00:01.443 |
0.0 |
OPTICS聚类算法演示 (../examples/cluster/plot_optics.py) |
00:01.410 |
0.0 |
使用树森林的特征重要性 (../examples/ensemble/plot_forest_importances.py) |
00:01.351 |
0.0 |
scikit-learn 1.3版本亮点 (../examples/release_highlights/plot_release_highlights_1_3_0.py) |
00:01.295 |
0.0 |
高斯混合模型选择 (../examples/mixture/plot_gmm_selection.py) |
00:01.295 |
0.0 |
3类别分类的概率校准 (../examples/calibration/plot_calibration_multiclass.py) |
00:01.214 |
0.0 |
不同核的先验和后验高斯过程图示 (../examples/gaussian_process/plot_gpr_prior_posterior.py) |
00:01.205 |
0.0 |
回归模型中目标转换的效果 (../examples/compose/plot_transformed_target.py) |
00:01.163 |
0.0 |
绘制不同SVM核的分类边界 (../examples/svm/plot_svm_kernels.py) |
00:01.151 |
0.0 |
压缩感知：使用L1先验 (Lasso) 的断层重建 (../examples/applications/plot_tomography_l1_reconstruction.py) |
00:01.148 |
0.0 |
梯度提升回归 (../examples/ensemble/plot_gradient_boosting_regression.py) |
00:01.142 |
0.0 |
具有混合类型的列转换器 (../examples/compose/plot_column_transformer_mixed_types.py) |
00:01.080 |
0.0 |
密集和稀疏数据上的Lasso (../examples/linear_model/plot_lasso_dense_vs_sparse_data.py) |
00:01.074 |
0.0 |
聚类性能评估中对偶然性的调整 (../examples/cluster/plot_adjusted_for_chance_measures.py) |
00:01.071 |
0.0 |
二分K-Means和常规K-Means性能比较 (../examples/cluster/plot_bisect_kmeans.py) |
00:01.053 |
0.0 |
特征缩放的重要性 (../examples/preprocessing/plot_scaling_importance.py) |
00:01.041 |
0.0 |
k-means初始化影响的经验评估 (../examples/cluster/plot_kmeans_stability_low_dim_dense.py) |
00:01.036 |
0.0 |
单个估计器与装袋：偏差-方差分解 (../examples/ensemble/plot_bias_variance.py) |
00:01.028 |
0.0 |
不同指标的凝聚聚类 (../examples/cluster/plot_agglomerative_clustering_metrics.py) |
00:00.998 |
0.0 |
可视化scikit-learn中的交叉验证行为 (../examples/model_selection/plot_cv_indices.py) |
00:00.998 |
0.0 |
管道化：链接PCA和逻辑回归 (../examples/compose/plot_digits_pipe.py) |
00:00.964 |
0.0 |
SVM平局打破示例 (../examples/svm/plot_svm_tie_breaking.py) |
00:00.947 |
0.0 |
k-means假设演示 (../examples/cluster/plot_kmeans_assumptions.py) |
00:00.912 |
0.0 |
使用轮廓分析选择KMeans聚类簇数 (../examples/cluster/plot_kmeans_silhouette_analysis.py) |
00:00.895 |
0.0 |
Lasso、Lasso-LARS和弹性网络路径 (../examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.py) |
00:00.844 |
0.0 |
scikit-learn 1.1版本亮点 (../examples/release_highlights/plot_release_highlights_1_1_0.py) |
00:00.821 |
0.0 |
半监督分类器与SVM在鸢尾花数据集上的决策边界比较 (../examples/semi_supervised/plot_semi_supervised_versus_svm_iris.py) |
00:00.818 |
0.0 |
Lasso模型选择：AIC-BIC / 交叉验证 (../examples/linear_model/plot_lasso_model_selection.py) |
00:00.813 |
0.0 |
绘制个体和投票回归预测 (../examples/ensemble/plot_voting_regressor.py) |
00:00.801 |
0.0 |
scikit-learn 1.5版本亮点 (../examples/release_highlights/plot_release_highlights_1_5_0.py) |
00:00.782 |
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) |
00:00.734 |
0.0 |
使用局部异常因子 (LOF) 进行新奇检测 (../examples/neighbors/plot_lof_novelty_detection.py) |
00:00.705 |
0.0 |
缓存最近邻 (../examples/neighbors/plot_caching_nearest_neighbors.py) |
00:00.671 |
0.0 |
带交叉验证的递归特征消除 (../examples/feature_selection/plot_rfe_with_cross_validation.py) |
00:00.661 |
0.0 |
GMM初始化方法 (../examples/mixture/plot_gmm_init.py) |
00:00.634 |
0.0 |
演示KBinsDiscretizer的不同策略 (../examples/preprocessing/plot_discretization_strategies.py) |
00:00.601 |
0.0 |
多类别接收者操作特征 (ROC) (../examples/model_selection/plot_roc.py) |
00:00.577 |
0.0 |
岭系数作为L2正则化的函数 (../examples/linear_model/plot_ridge_coeffs.py) |
00:00.571 |
0.0 |
两类别AdaBoost (../examples/ensemble/plot_adaboost_twoclass.py) |
00:00.571 |
0.0 |
稀疏逆协方差估计 (../examples/covariance/plot_sparse_cov.py) |
00:00.569 |
0.0 |
可视化VotingClassifier的概率预测 (../examples/ensemble/plot_voting_decision_regions.py) |
00:00.560 |
0.0 |
scikit-learn 0.23版本亮点 (../examples/release_highlights/plot_release_highlights_0_23_0.py) |
00:00.551 |
0.0 |
比较线性贝叶斯回归器 (../examples/linear_model/plot_ard.py) |
00:00.530 |
0.0 |
单调约束 (../examples/ensemble/plot_monotonic_constraints.py) |
00:00.529 |
0.0 |
比较随机森林和多输出元估计器 (../examples/ensemble/plot_random_forest_regression_multioutput.py) |
00:00.522 |
0.0 |
分位数回归 (../examples/linear_model/plot_quantile_regression.py) |
00:00.519 |
0.0 |
Theil-Sen回归 (../examples/linear_model/plot_theilsen.py) |
00:00.513 |
0.0 |
核PCA (../examples/decomposition/plot_kernel_pca.py) |
00:00.508 |
0.0 |
用于稀疏信号的L1模型 (../examples/linear_model/plot_lasso_and_elasticnet.py) |
00:00.475 |
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) |
00:00.468 |
0.0 |
简单的一维核密度估计 (../examples/neighbors/plot_kde_1d.py) |
00:00.461 |
0.0 |
用于图像分割的谱聚类 (../examples/cluster/plot_segmentation_toy.py) |
00:00.453 |
0.0 |
均值漂移聚类算法演示 (../examples/cluster/plot_mean_shift.py) |
00:00.451 |
0.0 |
主成分回归与偏最小二乘回归 (../examples/cross_decomposition/plot_pcr_vs_pls.py) |
00:00.443 |
0.0 |
特征聚集与单变量选择 (../examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py) |
00:00.434 |
0.0 |
高斯过程回归：基本入门示例 (../examples/gaussian_process/plot_gpr_noisy_targets.py) |
00:00.425 |
0.0 |
连接多个特征提取方法 (../examples/compose/plot_feature_union.py) |
00:00.413 |
0.0 |
IsolationForest示例 (../examples/ensemble/plot_isolation_forest.py) |
00:00.413 |
0.0 |
在硬币图像上演示结构化Ward层次聚类 (../examples/cluster/plot_coin_ward_segmentation.py) |
00:00.412 |
0.0 |
标签传播数字：主动学习 (../examples/semi_supervised/plot_label_propagation_digits_active_learning.py) |
00:00.407 |
0.0 |
谱系双聚类算法演示 (../examples/bicluster/plot_spectral_biclustering.py) |
00:00.393 |
0.0 |
收缩协方差估计：LedoitWolf vs OAS和最大似然 (../examples/covariance/plot_covariance_estimation.py) |
00:00.391 |
0.0 |
高斯混合模型正弦曲线 (../examples/mixture/plot_gmm_sin.py) |
00:00.389 |
0.0 |
因子分析 (带旋转) 以可视化模式 (../examples/decomposition/plot_varimax_fa.py) |
00:00.389 |
0.0 |
带AdaBoost的决策树回归 (../examples/ensemble/plot_adaboost_regression.py) |
00:00.382 |
0.0 |
使用成本复杂度剪枝对决策树进行后剪枝 (../examples/tree/plot_cost_complexity_pruning.py) |
00:00.380 |
0.0 |
L1惩罚和逻辑回归中的稀疏性 (../examples/linear_model/plot_logistic_l1_l2_sparsity.py) |
00:00.376 |
0.0 |
识别手写数字 (../examples/classification/plot_digits_classification.py) |
00:00.376 |
0.0 |
XOR数据集上的高斯过程分类 (GPC) 图示 (../examples/gaussian_process/plot_gpc_xor.py) |
00:00.371 |
0.0 |
2D点云上的FastICA与PCA比较 (../examples/decomposition/plot_ica_vs_pca.py) |
00:00.360 |
0.0 |
普通最小二乘和岭回归 (../examples/linear_model/plot_ols_ridge.py) |
00:00.357 |
0.0 |
带协方差椭球的线性和二次判别分析 (../examples/classification/plot_lda_qda.py) |
00:00.356 |
0.0 |
多项式和样条插值 (../examples/linear_model/plot_polynomial_interpolation.py) |
00:00.348 |
0.0 |
单类别SVM与使用随机梯度下降的单类别SVM (../examples/linear_model/plot_sgdocsvm_vs_ocsvm.py) |
00:00.347 |
0.0 |
使用FastICA进行盲源分离 (../examples/decomposition/plot_ica_blind_source_separation.py) |
00:00.342 |
0.0 |
分类器的概率校准 (../examples/calibration/plot_calibration.py) |
00:00.333 |
0.0 |
使用完全随机树的哈希特征转换 (../examples/ensemble/plot_random_forest_embedding.py) |
00:00.325 |
0.0 |
真实数据集上的异常值检测 (../examples/applications/plot_outlier_detection_wine.py) |
00:00.318 |
0.0 |
目标编码器的内部交叉拟合 (../examples/preprocessing/plot_target_encoder_cross_val.py) |
00:00.312 |
0.0 |
带交叉验证的接收者操作特征 (ROC) (../examples/model_selection/plot_roc_crossval.py) |
00:00.309 |
0.0 |
绘制岭系数作为正则化函数的函数 (../examples/linear_model/plot_ridge_path.py) |
00:00.309 |
0.0 |
决策树回归 (../examples/tree/plot_tree_regression.py) |
00:00.290 |
0.0 |
SVM-Anova：带单变量特征选择的SVM (../examples/svm/plot_svm_anova.py) |
00:00.283 |
0.0 |
正交匹配追踪 (../examples/linear_model/plot_omp.py) |
00:00.276 |
0.0 |
使用Display对象进行可视化 (../examples/miscellaneous/plot_display_object_visualization.py) |
00:00.262 |
0.0 |
使用预计算字典的稀疏编码 (../examples/decomposition/plot_sparse_coding.py) |
00:00.256 |
0.0 |
SVM：加权样本 (../examples/svm/plot_weighted_samples.py) |
00:00.251 |
0.0 |
标签传播数字：演示性能 (../examples/semi_supervised/plot_label_propagation_digits.py) |
00:00.249 |
0.0 |
亲和传播聚类算法演示 (../examples/cluster/plot_affinity_propagation.py) |
00:00.244 |
0.0 |
使用混淆矩阵评估分类器性能 (../examples/model_selection/plot_confusion_matrix.py) |
00:00.243 |
0.0 |
谱系协同聚类算法演示 (../examples/bicluster/plot_spectral_coclustering.py) |
00:00.238 |
0.0 |
使用贝叶斯岭回归进行曲线拟合 (../examples/linear_model/plot_bayesian_ridge_curvefit.py) |
00:00.229 |
0.0 |
稳健协方差估计和马哈拉诺比斯距离相关性 (../examples/covariance/plot_mahalanobis_distances.py) |
00:00.228 |
0.0 |
SGD：惩罚项 (../examples/linear_model/plot_sgd_penalties.py) |
00:00.227 |
0.0 |
高斯混合模型椭球体 (../examples/mixture/plot_gmm.py) |
00:00.199 |
0.0 |
增量PCA (../examples/decomposition/plot_incremental_pca.py) |
00:00.199 |
0.0 |
使用KBinsDiscretizer离散化连续特征 (../examples/preprocessing/plot_discretization.py) |
00:00.198 |
0.0 |
最近邻分类 (../examples/neighbors/plot_classification.py) |
00:00.184 |
0.0 |
F检验和互信息的比较 (../examples/feature_selection/plot_f_test_vs_mi.py) |
00:00.184 |
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) |
00:00.180 |
0.0 |
鸢尾花数据集的LDA和PCA 2D投影比较 (../examples/decomposition/plot_pca_vs_lda.py) |
00:00.174 |
0.0 |
最近邻回归 (../examples/neighbors/plot_regression.py) |
00:00.173 |
0.0 |
检测错误权衡 (DET) 曲线 (../examples/model_selection/plot_det.py) |
00:00.168 |
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) |
00:00.166 |
0.0 |
离散数据结构上的高斯过程 (../examples/gaussian_process/plot_gpr_on_structured_data.py) |
00:00.165 |
0.0 |
GMM协方差 (../examples/mixture/plot_gmm_covariances.py) |
00:00.165 |
0.0 |
K-Means和MiniBatchKMeans聚类算法比较 (../examples/cluster/plot_mini_batch_kmeans.py) |
00:00.164 |
0.0 |
欠拟合与过拟合 (../examples/model_selection/plot_underfitting_overfitting.py) |
00:00.162 |
0.0 |
绘制交叉验证预测 (../examples/model_selection/plot_cv_predict.py) |
00:00.160 |
0.0 |
比较交叉分解方法 (../examples/cross_decomposition/plot_compare_cross_decomposition.py) |
00:00.159 |
0.0 |
多标签分类 (../examples/miscellaneous/plot_multilabel.py) |
00:00.154 |
0.0 |
多维缩放 (../examples/manifold/plot_mds.py) |
00:00.151 |
0.0 |
单变量特征选择 (../examples/feature_selection/plot_feature_selection.py) |
00:00.148 |
0.0 |
使用可视化API绘制ROC曲线 (../examples/miscellaneous/plot_roc_curve_visualization_api.py) |
00:00.146 |
0.0 |
在LinearSVC中绘制支持向量 (../examples/svm/plot_linearsvc_support_vectors.py) |
00:00.146 |
0.0 |
比较带和不带邻域成分分析的最近邻 (../examples/neighbors/plot_nca_classification.py) |
00:00.144 |
0.0 |
set_output API介绍 (../examples/miscellaneous/plot_set_output.py) |
00:00.143 |
0.0 |
SVM：非平衡类别的分离超平面 (../examples/svm/plot_separating_hyperplane_unbalanced.py) |
00:00.142 |
0.0 |
DBSCAN聚类算法演示 (../examples/cluster/plot_dbscan.py) |
00:00.138 |
0.0 |
L1-逻辑回归的正则化路径 (../examples/linear_model/plot_logistic_path.py) |
00:00.138 |
0.0 |
显示管道 (../examples/miscellaneous/plot_pipeline_display.py) |
00:00.134 |
0.0 |
scikit-learn 1.7版本亮点 (../examples/release_highlights/plot_release_highlights_1_7_0.py) |
00:00.131 |
0.0 |
标签传播圆环：学习复杂结构 (../examples/semi_supervised/plot_label_propagation_structure.py) |
00:00.128 |
0.0 |
最近质心分类 (../examples/neighbors/plot_nearest_centroid.py) |
00:00.123 |
0.0 |
邻域成分分析图示 (../examples/neighbors/plot_nca_illustration.py) |
00:00.120 |
0.0 |
等渗回归 (../examples/miscellaneous/plot_isotonic_regression.py) |
00:00.119 |
0.0 |
带非线性核 (RBF) 的单类别SVM (../examples/svm/plot_oneclass.py) |
00:00.114 |
0.0 |
高斯过程分类 (GPC) 的等概率线 (../examples/gaussian_process/plot_gpc_isoprobability.py) |
00:00.112 |
0.0 |
绘制随机生成的多标签数据集 (../examples/datasets/plot_random_multilabel_dataset.py) |
00:00.110 |
0.0 |
HuberRegressor vs Ridge 在具有强异常值的数据集上 (../examples/linear_model/plot_huber_vs_ridge.py) |
00:00.103 |
0.0 |
特征聚类 (../examples/cluster/plot_digits_agglomeration.py) |
00:00.101 |
0.0 |
高斯混合模型的密度估计 (../examples/mixture/plot_gmm_pdf.py) |
00:00.099 |
0.0 |
scikit-learn 1.6 版本亮点 (../examples/release_highlights/plot_release_highlights_1_6_0.py) |
00:00.098 |
0.0 |
在 iris 数据集上绘制多类别 SGD (../examples/linear_model/plot_sgd_iris.py) |
00:00.094 |
0.0 |
通过信息准则进行 Lasso 模型选择 (../examples/linear_model/plot_lasso_lars_ic.py) |
00:00.080 |
0.0 |
SGD：凸损失函数 (../examples/linear_model/plot_sgd_loss_functions.py) |
00:00.080 |
0.0 |
使用 RANSAC 进行鲁棒线性模型估计 (../examples/linear_model/plot_ransac.py) |
00:00.076 |
0.0 |
理解决策树结构 (../examples/tree/plot_unveil_tree_structure.py) |
00:00.075 |
0.0 |
带有自定义核函数的 SVM (../examples/svm/plot_custom_kernel.py) |
00:00.074 |
0.0 |
绘制层次聚类树状图 (../examples/cluster/plot_agglomerative_dendrogram.py) |
00:00.071 |
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) |
00:00.064 |
0.0 |
SGD：加权样本 (../examples/linear_model/plot_sgd_weighted_samples.py) |
00:00.061 |
0.0 |
SGD：最大间隔分离超平面 (../examples/linear_model/plot_sgd_separating_hyperplane.py) |
00:00.055 |
0.0 |
非负最小二乘法 (../examples/linear_model/plot_nnls.py) |
00:00.052 |
0.0 |
SVM：最大间隔分离超平面 (../examples/svm/plot_separating_hyperplane.py) |
00:00.051 |
0.0 |
SVM 间隔示例 (../examples/svm/plot_svm_margin.py) |
00:00.050 |
0.0 |
元数据路由 (../examples/miscellaneous/plot_metadata_routing.py) |
00:00.049 |
0.0 |
K-Means++ 初始化示例 (../examples/cluster/plot_kmeans_plusplus.py) |
00:00.048 |
0.0 |
显示估计器和复杂管道 (../examples/miscellaneous/plot_estimator_representation.py) |
00:00.027 |
0.0 |
使用 FrozenEstimator 的示例 (../examples/frozen/plot_frozen_examples.py) |
00:00.016 |
0.0 |
管道 ANOVA SVM (../examples/feature_selection/plot_feature_selection_pipeline.py) |
00:00.014 |
0.0 |
scikit-learn 1.0 版本亮点 (../examples/release_highlights/plot_release_highlights_1_0_0.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 |
