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  2. 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.

  3. Python (programming language) - Wikipedia

    en.wikipedia.org/wiki/Python_(programming_language)

    Python 3.0, released in 2008, was a major revision not completely backward-compatible with earlier versions. Python 2.7.18, released in 2020, was the last release of Python 2. [37] Python consistently ranks as one of the most popular programming languages, and has gained widespread use in the machine learning community. [38] [39] [40] [41]

  4. scikit-learn - Wikipedia

    en.wikipedia.org/wiki/Scikit-learn

    scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...

  5. Code-excited linear prediction - Wikipedia

    en.wikipedia.org/wiki/Code-excited_linear_prediction

    Code-excited linear prediction (CELP) is a linear predictive speech coding algorithm originally proposed by Manfred R. Schroeder and Bishnu S. Atal in 1985. At the time, it provided significantly better quality than existing low bit-rate algorithms, such as residual-excited linear prediction (RELP) and linear predictive coding (LPC) vocoders (e.g., FS-1015).

  6. Online machine learning - Wikipedia

    en.wikipedia.org/wiki/Online_machine_learning

    Online learning is a common technique used in areas of machine learning where it is computationally infeasible to train over the entire dataset, requiring the need of out-of-core algorithms. It is also used in situations where it is necessary for the algorithm to dynamically adapt to new patterns in the data, or when the data itself is ...

  7. Machine learning - Wikipedia

    en.wikipedia.org/wiki/Machine_learning

    Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases).

  8. Predictive modelling - Wikipedia

    en.wikipedia.org/wiki/Predictive_modelling

    The first clinical prediction model reporting guidelines were published in 2015 (Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD)), and have since been updated. [10] Predictive modelling has been used to estimate surgery duration.

  9. Applied Predictive Technologies - Wikipedia

    en.wikipedia.org/wiki/Applied_Predictive...

    APT (now Mastercard) produces test and learn software for business analytics. [10] In February 2011, APT was awarded a patent that protects its core analytic technology for designing an in-market test and on matching test stores to control stores. [11] One of the company's patents was invalidated in 2020 by the US district court. [12]