Thank you for your message and pointing it out! I think it needs some clarification (I will update the documentations as well). The classification algorithm that you mentioned is SEFR, which is energy-efficient, but not as accurate as other algorithms. LinearBoost is the boosted version of SEFR, and it has superior F1 in 5 benchmark datasets over GBDTs. So, SEFR to LinearBoost is somehow like Decision Tree to CatBoost. SEFR is fast, and by boosting SEFR, we have LinearBoost which is slower but accurate. The results will be provided as a paper, but now, they are in the GitHub Repository's README file.