Binary-Class:
example-data: (1) guest: breast_hetero_guest.csv (2) host: breast_hetero_host.csv
dsl: test_secureboost_train_dsl.json
runtime_config: test_secureboost_train_binary_conf.json
Multi-Class:
example-data: (1) guest: vehicle_scale_hetero_guest.csv
(2) host: vehicle_scale_hetero_host.csv
dsl: test_secureboost_train_dsl.json
runtime_config: test_secureboost_train_multi_conf.json
Regression:
example-data: (1) guest: student_hetero_guest.csv
(2) host: student_hetero_host.csv
dsl: test_secureboost_train_dsl.json
runtime_config: test_secureboost_train_regression_conf.json
Multi-Host Regression
example-data: (1) guest: motor_hetero_guest.csv
(2) host1: motor_hetero_host_1.csv;
(3) host2: motor_hetero_host_2.csv
dsl: test_secureboost_train_dsl.json
runtime_config: test_secureboost_train_regression_multi_host_conf.json
Binary-Class With Missing Value
example-data: (1) guest: ionosphere_scale_hetero_guest.csv
(2) host: ionosphere_scale_hetero_host.csv
dsl: test_secureboost_train_dsl.json
runtime_config: test_secureboost_train_binary_with_missing_value_conf.json
This example also contains another two feature since FATE-1.1.
(1) evaluate data during training process, check the "validation_freqs" field in runtime_config
Early stopping example
example-data: (1) guest: student_hetero_guest.csv
(2) host: student_hetero_host.csv
dsl: test_secureboost_train_dsl.json
runtime_config: test_secureboost_train_with_early_stopping_conf.json
Binary-Class:
example-data: (1) guest: breast_hetero_guest.csv
(2) host: breast_hetero_guest.csv
dsl: test_secureboost_cross_validation_dsl.json
runtime_config: test_secureboost_cross_validation_binary_conf.json
Multi-Class:
example-data: (1) guest: vehicle_scale_hetero_guest.csv
(2) host: vehicle_scal_a.csv
dsl: test_secureboost_cross_validation_binary_conf.json
runtime_config: test_secureboost_cross_validation_multi_conf.json
Regression:
example-data: (1) guest: student_hetero_guest.csv
(2) host: student_hetero_host.csv
dsl: test_secureboost_cross_validation_dsl.json
runtime_config: test_secureboost_cross_validation_regression_conf.json
Users can use following commands to run a task.
flow job submit -c ${runtime_config} -d ${dsl}
Moreover, after successfully running the training task, you can use it to predict too.