Update README.md

README.md

@@ -33,7 +33,7 @@ data
 # Dataset & Samples
 In these files there are following fields:
 
-1) PARAMETERS OF SAMPLE SIMULATION ---
+1) PARAMETERS OF SAMPLE SIMULATION
 - **sample** is a type of the sample (train, val, test). These field is need to split dataset into train-validate-test samples for ML-model training;
 - **H0_H1** is a true hypothesis: if **H0**, then test statistics were simulated under S1(t)=S2(t); if **H1**, then test statistics were simulated under S1(t)≠S2(t);
 - **Hi** is an alternative hypothesis (H01-H09, H11-H19, or H21-H29) for S1(t) and S2(t). Detailed description of these alternatives can be found in the paper;
@@ -43,7 +43,7 @@ In these files there are following fields:
 - **real_perc1** is an actual censoring rate of sample 1;
 - **real_perc2** is an actual censoring rate of sample 2;
 
-2) STATISTICS OF CLASSICAL TWO-SAMPLE TESTS ---
+2) STATISTICS OF CLASSICAL TWO-SAMPLE TESTS
 - **Peto_test** is a statistic of the Peto and Peto’s Generalized Wilcoxon test (which is computed on two samples under parameters described above);
 - **Gehan_test** is a statistic of the Gehan’s Generalized Wilcoxon test;
 - **logrank_test** is a statistic of the logrank test;
@@ -61,12 +61,12 @@ In these files there are following fields:
 - **WLg_Prentice_test** is a statistic of the Weighted Logrank test (weighted function: 'Prentice');
 - **WKM_test** is a statistic of the Weighted Kaplan-Meier test;
 
-3) STATISTICS OF THE PROPOSED ML-METHODS FOR TWO-SAMPLE PROBLEM ---
+3) STATISTICS OF THE PROPOSED ML-METHODS FOR TWO-SAMPLE PROBLEM
 - **CatBoost_test** is a statistic of the proposed ML-method based on the CatBoost framework;
-- **XGBoost_test** ;
+- **XGBoost_test** is a statistic of the proposed ML-method based on the XGBoost framework;
 - **LightAutoML_test** is a statistic of the proposed ML-method based on the LightAutoML (LAMA) framework;
-- **SKLEARN_RF_test** ;
-- **SKLEARN_LogReg_test** ;
+- **SKLEARN_RF_test** is a statistic of the proposed ML-method based on Random Forest (implemented in sklearn);
+- **SKLEARN_LogReg_test** is a statistic of the proposed ML-method based on Logistic Regression (implemented in sklearn);
 - **SKLEARN_GB_test** are test statistics of the proposed ML-based methods.