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The scikit-multiflow library is implemented under the open research principles and is currently distributed under the BSD 3-clause license. scikit-multiflow is mainly written in Python, and some core elements are written in Cython for performance. scikit-multiflow integrates with other Python libraries such as Matplotlib for plotting, scikit-learn for incremental learning methods [4 ...
Data, context, and interaction (DCI) is a paradigm used in computer software to program systems of communicating objects. Its goals are: Its goals are: To improve the readability of object-oriented code by giving system behavior first-class status;
OpenML: [493] Web platform with Python, R, Java, and other APIs for downloading hundreds of machine learning datasets, evaluating algorithms on datasets, and benchmarking algorithm performance against dozens of other algorithms.
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 ...
CHAID is based on a formal extension of AID (Automatic Interaction Detection) [4] and THAID (THeta Automatic Interaction Detection) [5] [6] procedures of the 1960s and 1970s, which in turn were extensions of earlier research, including that performed by Belson in the UK in the 1950s. [7] In 1975, the CHAID technique itself was developed in ...
Relief is an algorithm developed by Kira and Rendell in 1992 that takes a filter-method approach to feature selection that is notably sensitive to feature interactions. [1] [2] It was originally designed for application to binary classification problems with discrete or numerical features. Relief calculates a feature score for each feature ...
Image segmentation via spectral graph partitioning by LOBPCG with multigrid preconditioning has been first proposed in [53] and actually tested in [54] and. [55] The latter approach has been later implemented in Python scikit-learn [56] that uses LOBPCG from SciPy with algebraic multigrid preconditioning for solving the eigenvalue problem for ...
In statistics, multivariate adaptive regression splines (MARS) is a form of regression analysis introduced by Jerome H. Friedman in 1991. [1] It is a non-parametric regression technique and can be seen as an extension of linear models that automatically models nonlinearities and interactions between variables.