CatBoost Object Importance was implemented using a CatBoost model with 100 iterations and a random seed according to the current replicate was used. The model was trained on the training set. The training sample influence on the external validation set called test-influence, was calculated, as well as the influence on all active training samples, which will be called self-influence. Data Valuation using Reinforcement Learning was implemented using the codebase from Yoon et al. The DVRL approach contains two prediction models: the data value estimator and the predictor. As the predictor, an LGBMClassifier with default parameters and a random seed according to the index of the current replicate was used. The data value estimator is implemented as a deep neural network, which depends on the parameters of the DVRL object. The DVRL object was initialized by setting the hidden dimensions to 100, the combined dimensions to 10, and the iterations to 1000. A Rectified Linear Unit (ReLU) function was used for activation, the learning rate was set to 0.01, and the batch size was set to 5000. The DVRL object was trained using the ROC-AUC-score as the performance measurement for the predictor, which was measured on the external validation set.