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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 ...
scikit-learn – extends SciPy with a host of machine learning models (classification, clustering, regression, etc.) Shogun (toolbox) – open-source, large-scale machine learning toolbox that provides several SVM (Support Vector Machine) implementations (like libSVM, SVMlight) under a common framework and interfaces to Octave, MATLAB, Python, R
The scikit-learn project started as scikits.learn, a Google Summer of Code project by David Cournapeau. After having worked for Silveregg, a SaaS Japanese company delivering recommendation systems for Japanese online retailers, [3] he worked for 6 years at Enthought, a scientific consulting company.
Tools used for controlling and improving business processes. Tools used for data consolidation and decision making. Nowadays, management tools have evolved dramatically in the last decade thanks to fast technology advances, so fast that it is difficult to select the best business tools for any situation in any company. [4]
Test management systems target manual effort and do not give all the required information. [1] Test automation management systems leverage automation efforts towards efficient and continuous processes of delivering test execution and new working tests by: Making transparent, meaningful and traceable reporting for all project stakeholders.
Business intelligence (BI) consists of strategies, methodologies, and technologies used by enterprises for data analysis and management of business information. [1] Common functions of BI technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text ...
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 ...
One of the early open-source AI frameworks was Scikit-learn, released in 2007. [28] Scikit-learn became one of the most widely used libraries for machine learning due to its ease of use and robust functionality, providing implementations of common algorithms like regression, classification, and clustering.