Dataset de Zenodo · Qarshiboyev Vosid Vaxob ugli
MACHINE LEARNING ALGORITHMS IN FORECASTING: A COMPARATIVE ANALYSIS OF LINEAR REGRESSION, RANDOM FOREST, AND XGBOOST
- Licencia
- CC BY 4.0uso comercial permitidoTexto de la licencia
- Esta licencia permite el uso comercial. Normalmente hay que citar al autor; consulta la licencia para ver las condiciones exactas.
- Formatos
- Tamaño
- 180 kB
- Actualizado
- 5 abr 2026
Descripción
Forecasting plays a crucial role in various domains such as finance, healthcare, energy, and economics. With the advancement of machine learning techniques, predictive accuracy has significantly improved compared to traditional statistical methods. This study presents a comparative analysis of three widely used machine learning algorithms—Linear Regression, Random Forest, and XGBoost—in forecasting tasks. Using benchmark datasets and standardized evaluation metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R² score, the performance of each algorithm is analyzed. The results demonstrate that ensemble-based models outperform linear models in capturing complex nonlinear relationships, while linear regression remains effective for interpretable and low-variance datasets.
Descripción de Zenodo, abreviada.
Archivos (1)
DataHunter no guarda ni aloja estos archivos: cada enlace lleva a la fuente original. Revisa allí la licencia antes de usar los datos.