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Scikit-Learn is widely known as a well-maintained, open source and popular Python ML tool, which contains comprehensive algorithm library included incremental learning [Scikit]. It extends the functionality of NumPy and SciPy packages with numerous DM algorithms. It also uses the Matplotlib package for plotting charts.
The Scikit-Learn project started as a Google Summer of Code project by David Cournapeau. Since 2015, it is under active development sponsored by INRIA, Telecom ParisTech and occasionally Google through the Google Summer of Code. Since April 2016, Scikit-Learn is provided in jointlydeveloped Anaconda [Anaconda] for Cloudera project on Hadoop clusters [AnaCloudera]. In addition to Scikit-Learn, Anaconda includes a number of popular packages for mathematics, science, and engineering for the Python ecosystem such as NumPy, SciPy and Pandas. Scikit-Learn provides access to the following sorts of functionality: classification, regression, clustering, dimensionality reduction, model selection and preprocessing.
Strong points
- General purpose, open source, commercially usable, well-maintained and popular Python ML tools.
- Support from big IT companies (Google) and institutions (INRIA).
- Well-updated and comprehensive set of algorithms and implementations.
- It is a part of many ecosystems; it is closely coupled with statistic and scientific Python packages.
Weak points
- Small datasets, API-oriented only, command-line interface requires Python programming skills.
- The library does not support GPU and has only basic tools for neural networks.
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