Research on applying machine learning models to predict and assess return on assets (ROA)
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s42107-024-01046-4.pdf
Reference33 articles.
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2. Breiman, L. (1999). Random forests—Random features. Computer Science, Mathematics. https://www.stat.berkeley.edu/~breiman/random-forests.pdf
3. Cai, Y., Yin, Qi., Qian, Su., Huang, X., Zhang, Y., & Liu, T. (2020). Prediction method of enterprise return on net assets based on improved random forest algorithm. Conference Series. https://doi.org/10.1088/1742-6596/1682/1/012083
4. Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science. https://doi.org/10.7717/peerj-cs.623
5. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics. https://doi.org/10.1214/aos/1013203451
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