In both techniques, grid search optimization is executed to determine the optimal hyperparameters for each algorithm, thereby enhancing its performance. Predictions either from the k -fold cross-validation and/or the single set of predictions, are stored in a dedicated folder. These predictions are then utilized to calculate metrics, which are then visualized using the metrics for regression, get confusion matrix and calculate confusion-based metrics functions from the workflow functions and classification metrics modules, respectively. The generated folders aid in organizing the predictions, enabling easy retrieval and analysis of the metrics, which offer valuable insights into the performance of the models and facilitate informed decision-making. The calculated metrics and visualizations generated by Pythi-aCHEM streamline the evaluation process, ensuring the comprehensive assessment of the model's performance.