. This change in investment decision uses decision tree analysis, where real options are developed as decision nodes, and …

This study evaluates the predictive performance of four models-Decision Tree, Random Forest, Prophet, and LSTM …. While both Decision Tree and Random Forest models yielded lower absolute errors compared to LSTM and Prophet, the Decision …, 52.1% on USEC, 43.5% on MED, and 22.7% on NEUR. These findings highlight the robustness of the Decision Tree model for …

neural networks (ANNs), and decision tree as machine learning techniques for predicting credit risk. It meticulously examines … compelling evidence endorsing ANNs and decision tree as more effective methods for credit risk prediction, thereby opening …

leverages Shapley values to generate AI predictions based on key explanatory variables. The decision tree and random forest … a larger dataset, where it maintained stable accuracy levels, with the decision tree and random forest models reaching …