In recent years, Machine Learning algorithms, in particular supervised learning techniques, have been shown to be very effective in solving regression problems. We compare the performance of a newly proposed regression algorithm against four conventional machine learning algorithms namely, Decision Trees, Random Forest, k-Nearest Neighbours and XG Boost. The proposed algorithm was presented in detail in a previous paper but detailed comparisons were not included. We do an in-depth comparison, using the Mean Absolute Error (MAE) as the performance metric, on a diverse set of datasets to illustrate the great potential and robustness of the proposed approach. The reader is free to replicate our results since we have provided the source code in a GitHub repository while the datasets are publicly available.

翻译：近年来，机器学习算法（尤其是监督学习技术）已被证明在解决回归问题方面非常有效。本文将一种新提出的回归算法与四种传统机器学习算法（即决策树、随机森林、k近邻和XGBoost）进行了性能对比。该算法在先前论文中已有详细阐述，但当时未包含详细的比较分析。我们以平均绝对误差（MAE）作为性能指标，在多个不同数据集上进行了深入对比，以展示所提方法的巨大潜力与鲁棒性。读者可自由复现我们的结果，因为源代码已上传至GitHub仓库，而数据集均为公开可用。