Comparative Performance Evaluation of Random Forest, Extreme Gradient Boosting and Linear Regression Algorithms Using Nigeria’s Gross Domestic Products
摘要
Statistical methods like linear regression analysis are frequently used to create predictive analytic models. However, these methods have limitations that may affect the accuracy of the models. Using a typical dataset, this study seeks to accomplish two main goals. First, we fitted three predictive models, including linear regression analysis and two ensemble machine learning algorithms: Random Forest Regressor and Extreme Gradient Boosting Regressor. Secondly, we compared the performance of the models using a 5-fold cross-validation technique. The Random Forest Regressor outperformed the other models, with a Mean Absolute Error (MAE) of 10.138, Mean Square Error (MSE) of 139.729, Mean Absolute Percentage Error (MAPE) of 0.071, Root Mean Square Error (RMSE) of 11.821, and Normalised Mean Square Error (NMSE) of 13.782. These results suggest that the Random Forest Regressor is optimal for developing predictive models with similar datasets.