CatBoost: Open-source gradient boosting library

by tosh • 2 years ago

43 13 2 years ago

biomcgary• 2 years ago

A year ago, I did a review of the the GBT algorithms and settled on LightGBM for predictive performance and CPU time (not GPU). The benchmark differences are small enough that I'm reluctant to change, but I'd love to hear feedback.

Has anyone ever created a meta-ensemble model of several GBT algorithms?

autokad• 2 years ago

I worked with catboost, lightgbm, and xgboost when doing the zillow kaggle competition a long time ago, 2016 I think. I used what is called blending, where you give a weight to the models such as:

.5 * xgboost + .25 * catboost + .25 * lightgbm

knightoffaith• 2 years ago

Interesting - did this work better than a single model? In general, do meta-ensembles work better? My sense was that just xgboost was the main winner of kaggle competitions.

Cyberdog• 2 years ago

I think me trying to understand AI terms that others seem to take for granted is giving me a good idea of what it feels like to my family when I try to talk to them about web development.

"Gradient boosting on decision trees?" Man, that's crazy. Hey, did you catch the game last night?

dmw_ng2• 2 years ago

Believe it or not this stuff is easy mode once you learn to ignore the terminology. It takes relatively little knowledge to know how to get data into the right format, set sensible training parameters and check results -- basically a handful of function calls. The trained models are also highly interpretable, you can literally plot them with Graphviz to understand how the model is treating values of each feature ("column")

hackerlight• 2 years ago

It's so easy to understand if every step is dumbed down and visualized. Teachers who write dense math on whiteboards need to learn a thing or two about pedagogy. The math should supplement the pictures, not the other way around, unless your audience is other experts.

redman25• 2 years ago

Easy to understand but hard to master. Any slip mixing training data in validation or testing data and your metrics are useless. It can be difficult to impossible to spot and give newbies false confidence in their model.

dang• 2 years ago

Yandex open sources CatBoost, a gradient boosting ML library - https://news.ycombinator.com/item?id=14795673 - July 2017 (28 comments)

CatBoost: Open-source gradient boosting library

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