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So, this looks really interesting and I look forward to delving into the methodology in order to understand the algorithm better. However, what I immediately no
by ImageXav 2y ago
So, this looks really interesting and I look forward to delving into the methodology in order to understand the algorithm better. However, what I immediately noticed from the paper linked in the documentation was that linearboost has a worse F1 score on average than the mentioned classifiers. Where it shines is energy consumption. Would it be possible to edit the title to reflect this? It's a huge gain in energy efficiency for a relatively small F1 loss, so kudos for that, but I think people might be expecting something a bit different from the title.
- hamid9 2y agoThank 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.
- ImageXav 2y agoAhhh I see, that makes sense. Thank you for clarifying I appreciate it. I made the mistake of assuming that the paper in the documentation was the paper of interest. I will take the time to properly delve in further once the paper is released, do you have any idea when that might be? In the mean time I look forward to giving testing the method on some toy examples I have.
- hamid9 2y agoThank you for bringing up this issue! Our plan is to release the paper in a month, but let's see how it goes. Feel free to reach out to me if you have any questions!