Performance Evaluation and Comparison of a New Regression Algorithm
Sabina Gooljar Kris Manohar Patrick Hosein
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.