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‎doc/2.0/fate/components/README.md‎

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For tutorial on running modules directly(without FATE-Client) with launcher,
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please refer [here](../ml/run_launchers.md).
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| Algorithm | Module Name | Examples | Description | Data Input | Data Output | Model Input | Model Output |
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|--------------------------------------------------|------------------------|------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------|-----------------------------------------------------------|-------------------------------|--------------|
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| [Reader](readme.md) | | | Component to passing namespace,name to downstream tasks | | output_data | | |
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| [PSI](psi.md) | PSI | [psi](../../../../examples/pipeline/psi) | Compute intersect data set of multiple parties without leakage of difference set information. Mainly used in hetero scenario task. | input_data | output_data | | |
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| [Sampling](sample.md) | Sample | [sample](../../../../examples/pipeline/sample) | Federated Sampling data so that its distribution become balance in each party.This module supports local and federation scenario. | input_data | output_data | | |
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| [Data Split](data_split.md) | DataSplit | [data split](../../../../examples/pipeline/data_split) | Split one data table into 3 tables by given ratio or count, this module supports local and federation scenario | input_data | train_output_data, validate_output_data, test_output_data | | |
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| [Feature Scale](feature_scale.md) | FeatureScale | [feature scale](../../../../examples/pipeline/feature_scale) | module for feature scaling and standardization. | train_data, test_data | train_output_data, test_output_data | input_model | output_model |
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| [Data Statistics](statistics.md) | Statistics | [statistics](../../../../examples/pipeline/statistics) | This component will do some statistical work on the data, including statistical mean, maximum and minimum, median, etc. | input_data | | | output_model |
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| [Hetero Feature Binning](feature_binning.md) | HeteroFeatureBinning | [hetero feature binning](../../../../examples/pipeline/hetero_feature_binning) | With binning input data, calculates each column's iv and woe and transform data according to the binned information. | train_data, test_data | train_output_data, test_output_data | input_model | output_model |
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| [Hetero Feature Selection](feature_selection.md) | HeteroFeatureSelection | [hetero feature selection](../../../../examples/pipeline/hetero_feature_selection) | Provide 3 types of filters. Each filters can select columns according to user config | train_data, test_data | train_output_data, test_output_data | input_models, input_model | output_model |
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| Algorithm | Module Name | Examples | Description | Data Input | Data Output | Model Input | Model Output |
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|--------------------------------------------------|------------------------|------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------|-----------------------------------------------------------|-----------------------------|--------------|
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| [Reader](readme.md) | | | Component to passing namespace,name to downstream tasks | | output_data | | |
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| [PSI](psi.md) | PSI | [psi](../../../../examples/pipeline/psi) | Compute intersect data set of multiple parties without leakage of difference set information. Mainly used in hetero scenario task. | input_data | output_data | | |
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| [Sampling](sample.md) | Sample | [sample](../../../../examples/pipeline/sample) | Federated Sampling data so that its distribution become balance in each party.This module supports local and federation scenario. | input_data | output_data | | |
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| [Data Split](data_split.md) | DataSplit | [data split](../../../../examples/pipeline/data_split) | Split one data table into 3 tables by given ratio or count, this module supports local and federation scenario | input_data | train_output_data, validate_output_data, test_output_data | | |
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| [Feature Scale](feature_scale.md) | FeatureScale | [feature scale](../../../../examples/pipeline/feature_scale) | module for feature scaling and standardization. | train_data, test_data | train_output_data, test_output_data | input_model | output_model |
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| [Data Statistics](statistics.md) | Statistics | [statistics](../../../../examples/pipeline/statistics) | This component will do some statistical work on the data, including statistical mean, maximum and minimum, median, etc. | input_data | | | output_model |
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| [Hetero Feature Binning](feature_binning.md) | HeteroFeatureBinning | [hetero feature binning](../../../../examples/pipeline/hetero_feature_binning) | With binning input data, calculates each column's iv and woe and transform data according to the binned information. | train_data, test_data | train_output_data, test_output_data | input_model | output_model |
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| [Hetero Feature Selection](feature_selection.md) | HeteroFeatureSelection | [hetero feature selection](../../../../examples/pipeline/hetero_feature_selection) | Provide 3 types of filters. Each filters can select columns according to user config | train_data, test_data | train_output_data, test_output_data | input_models, input_model | output_model |
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| [Coordinated-LR](logistic_regression.md) | CoordinatedLR | [coordinated LR](../../../../examples/pipeline/coordinated_lr) | Build hetero logistic regression model through multiple parties. | train_data, validate_data, test_data, cv_data | train_output_data, test_output_data, cv_output_datas | input_model, warm_start_model | output_model |
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| [Coordinated-LinR](linear_regression.md) | CoordinatedLinR | [coordinated LinR](../../../../examples/pipeline/coordinated_linr) | Build hetero linear regression model through multiple parties. | train_data, validate_data, test_data, cv_data | train_output_data, test_output_data, cv_output_datas | input_model, warm_start_model | output_model |
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| [Homo-LR](logistic_regression.md) | HomoLR | [homo lr](../../../../examples/pipeline/homo_lr) | Build homo logistic regression model through multiple parties. | train_data, validate_data, test_data, cv_data | train_output_data, test_output_data, cv_output_datas | input_model, warm_start_model | output_model |
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| [Homo-NN](homo_nn.md) | HomoNN | [homo nn](../../../../examples/pipeline/homo_nn) | Build homo neural network model through multiple parties. | train_data, validate_data, test_data, cv_data | train_output_data, test_output_data, cv_output_datas | input_model, warm_start_model | output_model |
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| [Hetero-NN](hetero_nn.md) | HeteroNN | [hetero nn](../../../../examples/pipeline/hetero_nn) | Build hetero neural network model through multiple parties. | train_data, validate_data, test_data | train_output_data, test_output_data | warm_start_model, input_model | output_model |
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| [Hetero Secure Boosting](hetero_secureboost.md) | HeteroSecureBoost | [hetero secureboost](../../../../examples/pipeline/hetero_secureboost) | Build hetero secure boosting model through multiple parties | train_data, test_data, cv_data | train_output_data, test_output_data, cv_output_datas | warm_start_model, input_model | output_model |
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| [Evaluation](evaluation.md) | Evaluation | [evaluation](../../../../examples/pipeline/hetero_secureboost) | Output the model evaluation metrics for user. | input_datas | | | |
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| [Union](union.md) | Union | [union](../../../../examples/pipeline/union) | Combine multiple data tables into one. | input_datas | output_data | | |
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| [Evaluation](evaluation.md) | Evaluation | [evaluation](../../../../examples/pipeline/hetero_secureboost) | Output the model evaluation metrics for user. | input_datas | | | |
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| [Union](union.md) | Union | [union](../../../../examples/pipeline/union) | Combine multiple data tables into one. | input_datas | output_data | | |
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| [SSHE-LR](logistic_regression.md) | SSHELR | [SSHE LR](../../../../examples/pipeline/sshe_lr) | Build hetero logistic regression model through two parties. | train_data, validate_data, test_data, cv_data | train_output_data, test_output_data, cv_output_datas | input_model, warm_start_model | output_model |
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| [SSHE-LinR](linear_regression.md) | SSHELinR | [SSHE LinR](../../../../examples/pipeline/sshe_linr) | Build hetero linear regression model through two parties. | train_data, validate_data, test_data, cv_data | train_output_data, test_output_data, cv_output_datas | input_model, warm_start_model | output_model |
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| [Feature Correlation](feature_correlation.md) | FeatureCorrelation | [Feature Correlation](../../../../examples/pipeline/feature_correlation) | Compute feature correlation locally or in hetero-federated setting. | input_data | | input_model | output_model |

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