Datasets:
metadata
license: cc-by-4.0
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:
- two_sample_problem_dataset.tsv.gz is a raw data. This file must be located in the "data/1_raw/";
- 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/";
- 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;
- 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.