  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.
