a465c220-53db-4c05-a55d-75328bb3f86a beie director@blueeyesintelligence.org 10.35940/ijitee.B9406.0212323 T. H. Davenport and J. G. Harris, "Competing on Analytics, Updated, with a New Introduction: The New Science of Winning.," Harvard Business School Press Books, p. 1, 2017, [Online]. Available: http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=124794328&site=ehost-live&scope=site%0Ahttp://hbr.org/product/a/an/10157-HBK-ENG10.1080/08839514.2018.1442991K. Koseler and M. Stephan, "Machine Learning Applications in Baseball: A Systematic Literature Review," Applied Artificial Intelligence, vol. 31, no. 9-10, pp. 745-763, 2017, doi: 10.1080/08839514.2018.1442991. [CrossRef]B. James, The Bill James Abstract. 1980.10.1080/10691898.1998.11910618M. R. Watnik, "Pay for Play: Are Baseball Salaries Based on Performance?," Journal of Statistics Education, vol. 6, no. 2, pp. 1-6, 1998, doi: 10.1080/10691898.1998.11910618. [CrossRef]10.3390/e24081138Y. Han, J. Kim, H. Keung, and T. Ng, "Logistic Regression Model for a Bivariate Binomial Distribution with Applications in Baseball Data Analysis," Entropy, 2022. [CrossRef]10.3390/e24020288S. Li, M. Huang, and Y. Li, "Exploring and Selecting Features to Predict the Next Outcomes of MLB Games," Entropy, 2022.10.1080/08839514.2018.1442991K. Koseler and M. Stephan, "Machine Learning Applications in Baseball: A Systematic Literature Review," Applied Artificial Intelligence, vol. 31, no. 9-10, pp. 745-763, 2017, doi: 10.1080/08839514.2018.1442991. [CrossRef]10.3390/app11104499M. Huang, "Use of Machine Learning and Deep Learning to Predict the Outcomes of Major League Baseball Matches," Applied sciences, 2021. [CrossRef]10.1109/ICNNB.2005.1614615H. Wang and D. Hu, "Comparison of SVM and LS-SVM for regression," in International Conference on Neural Networks and Brain Proceedings, 2005, vol. 1, pp. 279-283. doi: 10.1109/icnnb.2005.1614615. [CrossRef]10.1109/ISBAST.2013.50S. Wan and H. Yang, "Comparison among methods of ensemble learning," in Proceedings - 2013 International Symposium on Biometrics and Security Technologies, ISBAST 2013, 2013, pp. 286-290. doi: 10.1109/ISBAST.2013.50. [CrossRef]10.3390/w12113066G. Rong et al., "Rainfall Induced Landslide Susceptibility Mapping Based on Bayesian Optimized Random Forest and Gradient Boosting Decision Tree Models-A Case Study of Shuicheng County, China," Water (Basel), no. 3066, p. 12, 2020, doi: 10.3390/w12113066. [CrossRef]S. Dey, Y. Kumar, S. Saha, and S. Basak, "Forecasting to Classification : Predicting the direction of stock market price using Xtreme Gradient Boosting Forecasting to Classification : Predicting the direction of stock market price using Xtreme Gradient Boosting," in PESIT South Campus, 2016. doi: 10.13140/RG.2.2.15294.48968.10.1023/A:1007515423169E. Bauer and R. Kohavi, "An Empirical comparison of voting classification algorithms: bagging, boosting, and variants," Mach Learn, vol. 36, no. 1, pp. 105-139, 1999, doi: 10.1023/a:1007515423169. [CrossRef]10.1007/3-540-45014-9_1T. G. Dietterich, Ensemble methods in machine learning. 2000. doi: 10.1007/3-540-45014-9_1. [CrossRef]10.1155/2021/2730246R. K. Dhanaraj et al., "Random Forest Bagging and X-Means Clustered Antipattern Detection from SQL Query Log for Accessing Secure Mobile Data," Wirel Commun Mob Comput, vol. 2021, 2021, doi: 10.1155/2021/2730246. [CrossRef]10.3390/forecast3030029F. Petropoulos and E. Spiliotis, "The Wisdom of the Data: Getting the Most Out of Univariate Time Series Forecasting," Forecasting, vol. 3, no. 3, pp. 478-497, 2021, doi: 10.3390/forecast3030029. [CrossRef]10.1109/ICDM.2019.00124V. Grari, B. Ruf, S. Lamprier, and M. Detyniecki, "Fair adversarial gradient tree boosting," in Proceedings - IEEE International Conference on Data Mining, ICDM, 2019, vol. 2019-Novem, pp. 1060-1065. doi: 10.1109/ICDM.2019.00124. [CrossRef]IBM Cloud Education, "Neural Networks | IBM." 2020. [Online]. Available: https://www.ibm.com/cloud/learn/neural-networks10.3390/electronics10050593J. Zhou, A. H. Gandomi, F. Chen, and A. Holzinger, "Evaluating the quality of machine learning explanations: A survey on methods and metrics," Electronics (Switzerland), vol. 10, no. 5, pp. 1-19, 2021, doi: 10.3390/electronics10050593. [CrossRef]10.1088/1757-899X/324/1/012049W. Wang and Y. Lu, "Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model," IOP Conf Ser Mater Sci Eng, vol. 324, no. 1, 2018, doi: 10.1088/1757-899X/324/1/012049. [CrossRef]