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  2. CatBoost - Wikipedia

    en.wikipedia.org/wiki/Catboost

    CatBoost [6] is an open-source software library developed by Yandex. It provides a gradient boosting framework which, among other features, attempts to solve for categorical features using a permutation-driven alternative to the classical algorithm. [ 7 ]

  3. LightGBM - Wikipedia

    en.wikipedia.org/wiki/LightGBM

    Gradient-based one-side sampling (GOSS) is a method that leverages the fact that there is no native weight for data instance in GBDT. Since data instances with different gradients play different roles in the computation of information gain, the instances with larger gradients will contribute more to the information gain.

  4. XGBoost - Wikipedia

    en.wikipedia.org/wiki/XGBoost

    Soon after, the Python and R packages were built, and XGBoost now has package implementations for Java, Scala, Julia, Perl, and other languages. This brought the library to more developers and contributed to its popularity among the Kaggle community, where it has been used for a large number of competitions.

  5. pip (package manager) - Wikipedia

    en.wikipedia.org/wiki/Pip_(package_manager)

    pip (also known by Python 3's alias pip3) is a package-management system written in Python and is used to install and manage software packages. [4] The Python Software Foundation recommends using pip for installing Python applications and its dependencies during deployment. [5]

  6. Talk:CatBoost - Wikipedia

    en.wikipedia.org/wiki/Talk:Catboost

    Main page; Contents; Current events; Random article; About Wikipedia; Contact us; Donate

  7. Category : Python (programming language) scientific libraries

    en.wikipedia.org/wiki/Category:Python...

    Pages in category "Python (programming language) scientific libraries" The following 36 pages are in this category, out of 36 total. This list may not reflect recent changes .

  8. Retrieval-augmented generation - Wikipedia

    en.wikipedia.org/wiki/Retrieval-augmented_generation

    Retrieval-Augmented Generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to augment information drawn from its own vast, static training data.