Validation of the model

Model performance was tested with K-fold cross-validation with in our case we perform four rounds of validation (K = 4). One round of cross-validation involves portioning the dataset into complementary subsets, performing the training on one subset and the validation on the other. To reduce variability, multiple rounds of cross-validation are performed using different partitions, and the validation results are combined (averaged) over the rounds to give an estimate of the model’s predictive performance. The entire dataset is divided 4 times as 75% for training and 25% for validating the model. The results are then average across the 4 runs of training-validation. The model weights in the final model are obtained by using training dataset of the model. We measure the model’s performances at various threshold settings. We also used the area under the curve (AUC) of the receiver operating characteristic (ROC) curve as a threshold-invariant performance measure. Additionally, we report the model’s learning performances, i.e., how much data is required to reach the stability of the model. Learning is achieved when adding more data does not significantly impact the performance of the model.