Ula Sagarra

🔥🎉New library: boosting for survival analysis, including multiclass (competing risks)

Survival = missing outcomes because limited observation window (common in medicine, marketting...)

soda-inria.github.io/hazardous

Gives very fast boosted-trees for survival

10.03.2025 14:25 — 👍 45    🔁 11    💬 1    📌 0

#opensource #python #corporateresponsibility #sustainability | Marcelo Trylesinski 💭 Quick question: Is your company running Starlette or Uvicorn in production? If yes, you're among the millions benefiting from these projects FOR…

If possible, I'd appreciate a report on LinkedIn: www.linkedin.com/posts/marcel...

08.03.2025 13:10 — 👍 4    🔁 1    💬 0    📌 0

Picture of the study "Income-based U.S. household carbon footprints (1990–2019) offer new insights on emissions inequality and climate finance" Link in thread

We're being cooked in our own juices so that a bunch of trust fund babies could sip champagne on a super yacht while we choke on the ashes

24.12.2024 12:46 — 👍 2420    🔁 602    💬 47    📌 18

And from experience, they are the ones that are less plug and play. Lots of business alignment, data archeology and scientiffic know how + personalization. So difficult scenario for pure products as well. Maybe I'm thinking wishfully since it is our job, but I do believe this ;)

06.12.2024 12:35 — 👍 1    🔁 0    💬 0    📌 0

ML 'traditional' methods are still king in tabular tasks. And those are where internal company data is more valuable and ROI is higher (time series, fidelization, pricing...) and where explainability/control is a must. So I agree but there is space for many other options as well. Isn't it?

06.12.2024 10:08 — 👍 2    🔁 0    💬 1    📌 0