Stop Fighting Scikit-Learn Pipelines: How Metadata Routing Fixes Sample Weights & Groups
A couple of months ago, I stumbled upon this video by Vincent D. Warmerdam about metadata routing in scikit-learn. I'll be honest, I had no idea what "metadata routing" even meant, but Vincent's explanation completely changed how I think about building ML pipelines.
The video showed me that one of the most frustrating problems in scikit-learn; passing sample weights and groups through complex pipelines finally had an elegant solution. It piqued my curiosity enough that I dove deep into the feature, tested it extensively, and honestly, I was surprised by how little coverage this gets in technical blogs and articles. So I figured, why not write about it myself and share what I learned?
If you've ever struggled with imbalanced datasets, grouped cross-validation, or just wanted to pass custom information through your pipelines, this article is for you. Let's start from the very beginning.
What is "Metadata" in Machine Learning?
