Predicting Trip Purposes of Households in Makurdi Using Machine Learning: A Comparative Analysis of Decision Tree, CatBoost, and XGBoost Algorithms
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Abstract
This study explores the application of machine learning techniques for predicting trip purposes in Makurdi, Nigeria, utilizing three advanced algorithms: Decision Tree (DT), CatBoost, and XGBoost. The research aims to determine the most effective model for predicting household trip purposes based on demographic, socioeconomic, and travel data. Model performance was assessed using key metrics, including R-squared (R²), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), revealing distinct strengths and weaknesses among the models. CatBoost demonstrated the highest R² score of 73%, indicating its efficacy in capturing variance in trip purposes, despite a higher MAE (0.353) and RMSE (0.850), which suggest potential for larger prediction errors. XGBoost, with an R² score of 72% and the lowest RMSE of 0.545, exhibited a balanced performance, providing accurate predictions with minimal error. The Decision Tree model, while acceptable with an R² of 68%, MAE of 0.314, and RMSE of 0.615, ranked lower in predictive accuracy. The findings advocate for the use of XGBoost as the most reliable model for this task. Future research directions include hyperparameter optimization and the investigation of ensemble methods to enhance predictive accuracy
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