Papers › A simple guide from Machine Learning outputs to statistical criteria
A simple guide from Machine Learning outputs to statistical criteria
Charanjit K. Khosa, Veronica Sanz, Michael Soughton
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In this paper we propose ways to incorporate Machine Learning training outputs into a study of statistical significance. We describe these methods in supervised classification tasks using a CNN and a DNN output, and unsupervised learning based on a VAE. As use cases, we consider two physical situations where Machine Learning are often used: high-p_T hadronic activity, and boosted Higgs in association with a massive vector boson.
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