| @@ -42,37 +42,43 @@ export const regressorAlgorithms = [ | |||||
| // 特征预处理算法 | // 特征预处理算法 | ||||
| export const featureAlgorithms = [ | export const featureAlgorithms = [ | ||||
| { label: 'densifier (数据增稠)', value: 'densifier' }, | |||||
| { label: 'densifier (缺失值填充)', value: 'densifier' }, | |||||
| { | { | ||||
| label: 'extra_trees_preproc_for_classification (分类任务极端随机树)', | |||||
| label: 'extra_trees_preproc_for_classification (特征选择-分类任务极端随机树)', | |||||
| value: 'extra_trees_preproc_for_classification', | value: 'extra_trees_preproc_for_classification', | ||||
| }, | }, | ||||
| { | { | ||||
| label: 'extra_trees_preproc_for_regression (回归任务极端随机树)', | |||||
| label: 'extra_trees_preproc_for_regression (特征选择-回归任务极端随机树)', | |||||
| value: 'extra_trees_preproc_for_regression', | value: 'extra_trees_preproc_for_regression', | ||||
| }, | }, | ||||
| { label: 'fast_ica (快速独立成分分析)', value: 'fast_ica' }, | |||||
| { label: 'feature_agglomeration (特征聚合)', value: 'feature_agglomeration' }, | |||||
| { label: 'kernel_pca (核主成分分析)', value: 'kernel_pca' }, | |||||
| { label: 'kitchen_sinks (随机特征映射)', value: 'kitchen_sinks' }, | |||||
| { label: 'liblinear_svc_preprocessor (线性svc预处理器)', value: 'liblinear_svc_preprocessor' }, | |||||
| { label: 'fast_ica (特征选择-快速独立成分分析)', value: 'fast_ica' }, | |||||
| { label: 'feature_agglomeration (特征变换-特征聚合)', value: 'feature_agglomeration' }, | |||||
| { label: 'kernel_pca (特征选择-核主成分分析)', value: 'kernel_pca' }, | |||||
| { label: 'kitchen_sinks (特征变换-随机特征映射)', value: 'kitchen_sinks' }, | |||||
| { | |||||
| label: 'liblinear_svc_preprocessor (特征选择-线性svc预处理器)', | |||||
| value: 'liblinear_svc_preprocessor', | |||||
| }, | |||||
| { label: 'no_preprocessing (无预处理)', value: 'no_preprocessing' }, | { label: 'no_preprocessing (无预处理)', value: 'no_preprocessing' }, | ||||
| { label: 'nystroem_sampler (尼斯特罗姆采样器)', value: 'nystroem_sampler' }, | |||||
| { label: 'pca (主成分分析)', value: 'pca' }, | |||||
| { label: 'polynomial (多项式特征扩展)', value: 'polynomial' }, | |||||
| { label: 'random_trees_embedding (随机森林特征嵌入)', value: 'random_trees_embedding' }, | |||||
| { label: 'nystroem_sampler (特征变换-尼斯特罗姆采样器)', value: 'nystroem_sampler' }, | |||||
| { label: 'pca (特征选择-主成分分析)', value: 'pca' }, | |||||
| { label: 'polynomial (特征变换-多项式特征扩展)', value: 'polynomial' }, | |||||
| { label: 'random_trees_embedding (特征变换-随机森林特征嵌入)', value: 'random_trees_embedding' }, | |||||
| { | { | ||||
| label: 'select_percentile_classification (基于百分位的分类特征选择)', | |||||
| label: 'select_percentile_classification 特征选择-基于百分位的分类特征选择)', | |||||
| value: 'select_percentile_classification', | value: 'select_percentile_classification', | ||||
| }, | }, | ||||
| { | { | ||||
| label: 'select_percentile_regression (基于百分位的回归特征选择)', | |||||
| label: 'select_percentile_regression (特征选择-基于百分位的回归特征选择)', | |||||
| value: 'select_percentile_regression', | value: 'select_percentile_regression', | ||||
| }, | }, | ||||
| { | { | ||||
| label: 'select_rates_classification (基于比率的分类特征选择)', | |||||
| label: 'select_rates_classification (特征选择-基于比率的分类特征选择)', | |||||
| value: 'select_rates_classification', | value: 'select_rates_classification', | ||||
| }, | }, | ||||
| { label: 'select_rates_regression (基于比率的回归特征选择)', value: 'select_rates_regression' }, | |||||
| { label: 'truncatedSVD (截断奇异值分解)', value: 'truncatedSVD' }, | |||||
| { | |||||
| label: 'select_rates_regression (特征选择-基于比率的回归特征选择)', | |||||
| value: 'select_rates_regression', | |||||
| }, | |||||
| { label: 'truncatedSVD (特征变换-截断奇异值分解)', value: 'truncatedSVD' }, | |||||
| ]; | ]; | ||||