Passing metadata such as sample_weight and groups through a scikit-learn cross_validate, GridSearchCV, or a Pipeline to the right estimators, scorers, and CV splitters has been either cumbersome, hacky, or impossible. The new metadata routing mechanism in scikit-learn enables you to pass metadata through these objects. As a use-case, we study how you can implement a revenue sensitive scoring while doing a hyperparameter search within a GridSearchCV object.
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Speaker
Adrin
1
talks
cofounder
probabl.ai
👨💻 Adrin is a scikit-learn maintainer and works on a few other open source projects. He has a PhD in Bioinformatics, has worked as a consultant, and in an algorithmic privacy and fairness team. He is now a cofounder at probabl.ai, where they work on enabling people to do statistically sane machine learning.
Bio from: May Members Talk evening
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