README.md

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license: cc-by-4.0
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TITLE: "Machine Learning for Two-Sample Testing under Right-Censored Data: A Simulation Study"

AUTHORS:
- PETR PHILONENKO, Ph.D. in Computer Science;
- SERGEY POSTOVALOV, D.Sc. in Computer Science.

This dataset is a supplement to the github-project published in the https://github.com/pfilonenko/ML_for_TwoSampleTesting. This dataset contains following files:
1) **two_sample_problem_dataset.tsv.gz** is a raw data. This file must be located in the "ML_for_TwoSampleTesting/proposed_ml_for_two_sample_testing
/data/1_raw/";
2) **sample_train.tsv.gz** and **sample_simulation.tsv.gz** are train and test samples splited from the **two_sample_problem_dataset.tsv.gz**. These files must be located in the "ML_for_TwoSampleTesting/proposed_ml_for_two_sample_testing
/data/2_samples/";
3) **dataset_with_ML_pred.tsv.gz** is the test sample supplemented by the predictions of the proposed ML-methods. This file must be located in "ML_for_TwoSampleTesting/proposed_ml_for_two_sample_testing
/data/3_dataset_with_ML_pred/".

In these files there are following fields:
- **sample** is a sample type (train, val, test);
- **H0_H1** is a true hypothesis (H0 or H1);
- **Hi** is an alternative hypothesis (H01-H09, H11-H19, or H21-H29);
- **n1** is the size of sample 1;
- **n2** is the size of sample 2;
- **real_perc1** is an actual censoring rate of sample 1;
- **real_perc2** is an actual censoring rate of sample 2;
- **perc** is the set censoring rate for the samples 1 and 2;
- **Peto_test**, **Gehan_test**, **logrank_test**, **CoxMantel_test**, **BN_GPH_test**, **BN_MCE_test**, **BN_SCE_test**, **Q_test**, **MAX_Value_test**, **MIN3_test**, **WLg_logrank_test**, **WLg_TaroneWare_test**, **WLg_Breslow_test**, **WLg_PetoPrentice_test**, **WLg_Prentice_test**, **WKM_test** are test statistics of classical two-sample tests under right-censored data;
- **CatBoost_test**, **XGBoost_test**, **LightAutoML_test**, **SKLEARN_RF_test**, **SKLEARN_LogReg_test**, **SKLEARN_GB_test** are test statistics of the proposed ML-based methods.