Constant Bridon


Constant is strongly interested in the creation of value out of data, and he is currently experiencing one of the most satisfying mind shifts of the past 10 years. Companies start to realize that data is not a useless expense to build on, but a real opportunity to assess their results, find insights in their process failures, reclaim their expertise and probably evolve to a more sustainable business. He wants to help those who believe in such a potential by accelerating their transition toward a data driven company. In order to address these new problematics, Constant focuses on mastering every skill of a complete Data Geek : architecture expertise (data, applications, network), data science mastering (statistical learning, data visualisation, algorithmic theory), customer and business understanding (model prediction consumption, business metrics, customer needs).
He has been working for about two years for OCTO Technology in the best Big Data team in France. Constant is an expert in the industry sector and he works on several types of mission, ranging from predictive maintenance of production site, to prediction of critical KPIs in video games, via real time monitoring of manufacturing devices. Prior to joining OCTO, he was working as a researcher in data61 (formerly known as NICTA), the best research institute in ICT in Australia on applying Machine Learning to profile GUI users and provide the best amount of information to help them make a decision based on a machine learning prediction. Constant published his work in two major conferences: CHI WIP 2015 and OzCHI ’15.

Topic: Feature importance in ensemble methods : understanding the prediction thanks to your variables

Ensemble methods are extremely performant in terms of prediction, but lack easy interpretation. Feature importance is not only counting up how many times a feature has been used in a weak learner, but also by how much this feature contributes to the result. Moreover, feature importance is strongly linked to the problem at stake (regression, classification), and the algorithm used. Constant mainly focused his work around gradient boosting implementation, and provide a relevant metric for feature importance and prediction interpretation for several typical use cases. He also benchmarkes this metric with other agnostic approaches.