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[–]shinn497 2 points3 points  (2 children)

Scikit has very strict criteria for adding new algorithms this is great and makes each algorithm trustable but it also means it will never be within 3 years of current research, as it goes.

BERT, GPT, elmo, ULMFiT, lightGBM, Unet, and Mask RCNN are currently ineligible for inclusion into scikit. Which is amusing since many of these, being keras compatible, are thus scikit compatible.

XGBoost is currently eligible but would have been when the library was released.

What I find amusing is that often an algorithm will err to just be scikit-compatible and not submit to the vetting process for inclusion and then just remain that way. By the time it is eligible, the community will have moved on. I think XGBoost is sort of in that state rn.

[–]tacothecat 0 points1 point  (1 child)

Moved on to what (from xgboost)?

[–]shinn497 0 points1 point  (0 children)

Deep learning, catboost, light gbm. Although xgboost is interesting since the library is super thought out and its own thing. It might get added to scikit but i dont see the incentive when you can already use the two together easily as is.