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tods/common-primitives/pipelines
Daochen Zha df1d62a92f Init 5 years ago
..
classification.light_gbm.DataFrameCommon Init 5 years ago
classification.random_forest.DataFrameCommon Init 5 years ago
classification.xgboost_dart.DataFrameCommon Init 5 years ago
classification.xgboost_gbtree.DataFrameCommon Init 5 years ago
data_augmentation.datamart_augmentation.Common Init 5 years ago
data_preprocessing.dataset_sample.Common Init 5 years ago
data_preprocessing.one_hot_encoder.MakerCommon Init 5 years ago
data_preprocessing.one_hot_encoder.PandasCommon Init 5 years ago
data_transformation.column_parser.DataFrameCommon Init 5 years ago
data_transformation.construct_predictions.DataFrameCommon Init 5 years ago
data_transformation.dataset_to_dataframe.Common Init 5 years ago
data_transformation.extract_columns.Common Init 5 years ago
data_transformation.extract_columns_by_semantic_types.DataFrameCommon Init 5 years ago
data_transformation.extract_columns_by_structural_types.Common Init 5 years ago
data_transformation.grouping_field_compose.Common Init 5 years ago
data_transformation.horizontal_concat.DataFrameConcat Init 5 years ago
data_transformation.remove_columns.Common Init 5 years ago
data_transformation.rename_duplicate_name.DataFrameCommon Init 5 years ago
evaluation.kfold_timeseries_split.Common Init 5 years ago
operator.dataset_map.DataFrameCommon Init 5 years ago
regression.xgboost_gbtree.DataFrameCommon Init 5 years ago
schema_discovery.profiler.Common Init 5 years ago
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全栈的自动化机器学习系统，主要针对多变量时间序列数据的异常检测。TODS提供了详尽的用于构建基于机器学习的异常检测系统的模块，它们包括：数据处理（data processing），时间序列处理（ time series processing），特征分析（feature analysis)，检测算法（detection algorithms），和强化模块（ reinforcement module）。这些模块所提供的功能包括常见的数据预处理、时间序列数据的平滑或变换，从时域或频域中抽取特征、多种多样的检测算

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