🔥🎉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
If possible, I'd appreciate a report on LinkedIn: www.linkedin.com/posts/marcel...
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
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 ;)
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?
