Weeknotes: 19th October 2026

Pondering test data selection Last week I talked about two strands of work on the habitat map work: improving the robustness of my validation approach, and integrating eBird into my occurrence data alongside that from GBIF. I spent some time wrapping up those bits, and am now quite pleased with where we are. One interesting thing that fell out of all this was there's a subtle but interesting decision point as to when in a pipeline to split out your test data from your training data. Looking back at what I was doing previously and comparing that with other published work that validates itself in a similar way, I suspect that I'm not alone in this error because it's a slightly easy trap to fall into. Let me explain. In this pipeline, like many other pipelines, I'm taking some samples from the real world, cleaning them up to try to remove problems with the real world source data, and then applying that to a statistical model (in this case, it's a machine learning model, but that's still…