Traceable AI with Random Forest Reasoning
Abstract: The potent ensemble learning method Random Forest (RF) is frequently applied to tasks involving regression and classification. Its intrinsic black-box character, however, makes it challenging to understand how decisions are made. The reasoning underlying Random Forest predictions is traced and interpreted using a variety of methods in this research. We provide a summary of surrogate models, SHAP values, LIME, feature importance techniques, and decision path analysis to improve interpretability. In addition, we go over parallels with various machine learning models, explainability issues in high-stakes domains, and practical applications. Finally, we investigate potential avenues for further study to increase Random Forest models? transparency.
Keywords: Random Forest, Machine Learning, Interpretability, Feature Importance, Decision Path, Surrogate Models, SHAP, LIME, XGBoost, Explainability
How to Cite?: Pushkar Vashishtha, "Traceable AI with Random Forest Reasoning", Volume 14 Issue 7, July 2025, International Journal of Science and Research (IJSR), Pages: 1915-1920, https://www.ijsr.net/getabstract.php?paperid=SR25629090951, DOI: https://dx.doi.org/10.21275/SR25629090951