A fast xgboost feature selection algorithm
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
Python implementations of the Boruta all-relevant feature selection method.
2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
🦖 X—LLM: Cutting Edge & Easy LLM Finetuning
3 captures since 2026-05-22
