What "evidence-based" actually means here
Most machine-learning tutorials are written for software engineers, with examples about spam filters and photos of cats. This is not that. Every module is built for physicists, uses the kind of data you already work with, and is grounded in the peer-reviewed physics literature — the same papers your reviewers will expect you to know.
The idea is not new or fringe. For two decades, physicists have shown that these methods separate signal from background better than hand-tuned cuts, and the field built standard tools around them. We do not invent statistics or hand-wave the hard parts. We show you the method, show you the evidence, and show you exactly how to check your own work before anyone else does.
Start with the free module
Get Module 1 and the one-page Roadmap — the exact path from where you are now to a finished, defensible analysis. No payment, no pressure.
Get Module 1 + the Roadmap →Scientific references (5)
- Roe, B. P., Yang, H.-J., Zhu, J., Liu, Y., Stancu, I., & McGregor, G. (2005). Boosted decision trees as an alternative to artificial neural networks for particle identification. Nuclear Instruments and Methods in Physics Research A, 543(2-3), 577–584.
- Hoecker, A. et al. (2007). TMVA — Toolkit for Multivariate Data Analysis. arXiv:physics/0703039.
- Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.
- Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.
- Adam-Bourdarios, C., Cowan, G., Germain, C., Guyon, I., Kégl, B., & Rousseau, D. (2015). The Higgs boson machine learning challenge. JMLR Workshop and Conference Proceedings, 42, 19–55.