scikit-learn Advanced Data Science Library

A scikit-learn Advanced Data Science Library is an open source Python-based advanced data science library.

• Context:
  • It can (typically) support a scikit-learn Function.
  • It can (typically) depend on a SciPy Library, and a numpy Library.
  • It can (typically) be covered by a BSD License.
  • It can be used to create a scikit-learn Program.
• Example(s):
  • scikit-learn v0.18.0[1], ~2016-09-28
  • scikit-learn v0.16.1, ~2015-03
  • scikit-learn v0.15.1, ~2014-08.
  • scikit-learn v0.14.1, ~2013-08.
  • http://scikit-learn.org/stable/whats_new.html
• Counter-Example(s):
  • Spark MLlib.
  • Weka.
  • Mahout.
  • Torch Library.
  • caret R Package.
  • SVMight, LIBSVM, ...
• See: Gradient Boosting, DBSCAN, Statsmodels, sklearn.linear_model.

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References

2017b

• https://medium.com/@kailashahirwar/essential-cheat-sheets-for-machine-learning-and-deep-learning-researchers-efb6a8ebd2e5
  • QUOTE:

2014

• (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/scikit-learn Retrieved:2014-7-27.
  • scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

    It features various classification, regression and clustering algorithms including support vector machines, logistic regression, naive Bayes, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

2014b

• http://scikit-learn.org/
  • Simple and efficient tools for data mining and data analysis
  • Accessible to everybody, and reusable in various contexts
  • Built on NumPy, SciPy, and matplotlib
  • Open source, commercially usable - BSD license

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