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  2. Name mangling - Wikipedia

    en.wikipedia.org/wiki/Name_mangling

    Python's runtime does not restrict access to such attributes, the mangling only prevents name collisions if a derived class defines an attribute with the same name. On encountering name mangled attributes, Python transforms these names by prepending a single underscore and the name of the enclosing class, for example: >>>

  3. CatBoost - Wikipedia

    en.wikipedia.org/wiki/Catboost

    It works on Linux, Windows, macOS, and is available in Python, [8] R, [9] and models built using CatBoost can be used for predictions in C++, Java, [10] C#, Rust, Core ML, ONNX, and PMML. The source code is licensed under Apache License and available on GitHub. [6] InfoWorld magazine awarded the library "The best machine learning tools" in 2017.

  4. Name–value pair - Wikipedia

    en.wikipedia.org/wiki/Name–value_pair

    Example of a web form with name-value pairs. A name–value pair, also called an attribute–value pair, key–value pair, or field–value pair, is a fundamental data representation in computing systems and applications. Designers often desire an open-ended data structure that allows for future extension without modifying existing code or data.

  5. Priority queue - Wikipedia

    en.wikipedia.org/wiki/Priority_queue

    STL also has utility functions for manipulating another random-access container as a binary max-heap. The Boost libraries also have an implementation in the library heap. Python's heapq module implements a binary min-heap on top of a list. Java's library contains a PriorityQueue class, which implements a min-priority-queue as a binary heap.

  6. 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.

  7. Snake case - Wikipedia

    en.wikipedia.org/wiki/Snake_case

    C++, Boost [10] C, for some type names in the standard library, but not for function names. Eiffel, for class and feature names [11] Elixir, for atom, variable, and function names [12] Erlang, for function names [13] GDScript, for variable and function names [14] Java uses SCREAMING_SNAKE_CASE for static final constants and enum values. [15]

  8. Boosting (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Boosting_(machine_learning)

    scikit-learn, an open source machine learning library for Python; Orange, a free data mining software suite, module Orange.ensemble; Weka is a machine learning set of tools that offers variate implementations of boosting algorithms like AdaBoost and LogitBoost

  9. Gradient boosting - Wikipedia

    en.wikipedia.org/wiki/Gradient_boosting

    Gradient boosting is a machine learning technique based on boosting in a functional space, where the target is pseudo-residuals instead of residuals as in traditional boosting.