Olivier Grisel
Olivier is a software engineer at Probabl and a core contributor to the scikit-learn open source Machine Learning library.
Probabl
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Data scientists are repeatedly told that it is absolutely critical to align their model training methodology with a specific business objective. While being a rather good advice, it usually falls short on details on how to achieve this in practice.
This hands-on tutorial aims to introduce helpful theoretical concepts and concrete software tools to help them bridge this gap. This method will be illustrated on a worked practical use case: optimizing the operations of a fraud detection system for a payment processing platform.
More specifically, we will introduce the concepts of calibrated probabilistic classifiers, how to evaluate them and fix common causes of mis-calibration. In a second part, we will explore how to turn probabilistic classifiers into optimal business decision makers.
The tutorial material is available at the following URL: https://github.com/probabl-ai/calibration-cost-sensitive-learning