References
Bergstra, James, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl. 2011.
“Algorithms for Hyper-Parameter Optimization.” Advances
in Neural Information Processing Systems 24.
Bergstra, James, and Yoshua Bengio. 2012. “Random Search for
Hyper-Parameter Optimization.” Journal of Machine Learning
Research 13 (10): 281–305. http://jmlr.org/papers/v13/bergstra12a.html.
Calvo, J., and M. Fernandez. 2024. “Enhancing Customer Retention
with Machine Learning: A Comparative Study of Ensemble Models.”
Journal of Retail Analytics. https://www.sciencedirect.com/science/article/pii/S2667096825000138.
Hussain, A., and F. Al-Obeidat. 2024. “A Machine Learning Model to
Predict Heart Failure Readmission: Toward Optimal Feature Set.”
Frontiers in Artificial Intelligence 7: 1363226. https://doi.org/10.3389/frai.2024.1363226.
Khan, A., and R. Malik. 2024. “Predictive Power of Random Forests
in Analyzing Risk Management Practices and Concerns in Islamic
Banks.” Journal of Risk and Financial Management 17 (3):
104. https://doi.org/10.3390/jrfm17030104.
Kim, H., and S. Park. 2021. “Machine Learning for Predicting
Readmission Risk Among Hospitalized Patients: A Systematic
Review.” Digital Health. https://www.sciencedirect.com/science/article/pii/S2666389921002622.
Li, H., and Y. Zhang. 2021. “Financial Credit Risk Control
Strategy Based on Weighted Random Forest Algorithm.” Journal
of Applied Mathematics, 1–9. https://doi.org/10.1155/2021/6276155.
Schmidt, L., and M. Hoffmann. 2023. “Using Machine Learning
Prediction Models for Quality Control: A Case Study from the Automotive
Industry.” Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10287-023-00448-0.
Wang, L., and J. Chen. 2023. “Customer Churn Prediction Based on
the Decision Tree and Random Forest Model.” International
Journal of Computer Applications. https://www.researchgate.net/publication/370571328_Customer_Churn_Prediction_Based_on_the_Decision_Tree_and_Random_Forest_Model.
Zhao, Q. 2024. “Random Forest-Based Machine Failure Prediction in
Industrial Equipment.” Applied Sciences 15 (16): 8841.
https://doi.org/10.3390/app15168841.