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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 "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 "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 "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; Values of classical two-sample tests under right-censored data:
  • 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 Values of the proposed ML-based methods:
  • CatBoost_test
  • XGBoost_test
  • LightAutoML_test
  • SKLEARN_RF_test
  • SKLEARN_LogReg_test
  • SKLEARN_GB_test