Scikit-learn is a free open-source machine learning library for the Python programming language. It features various algorithms, including supervised and unsupervised learning, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. It is licensed under the BSD license and is commercially usable. Scikit-learn is widely used in academia, finance, and industry. (1)
Scikit-learn provides simple and efficient tools for data mining and data analysis. It is built on NumPy, SciPy, and matplotlib and integrates well with these libraries. Scikit-learn consists of a collection of supervised and unsupervised learning algorithms such as support vector machines, random forests, and k-nearest neighbors. It also contains tools for dimensionality reduction, feature selection, and preprocessing. (2)
Scikit-learn is a powerful tool for building and training predictive models. It provides a range of supervised and unsupervised learning algorithms and also supports cross-validation, model calibration, and prediction. Scikit-learn makes it easy to use and evaluate different models and provides an extensive set of metrics to evaluate model performance. (3)
Scikit-learn is widely used in a range of areas such as finance, healthcare, marketing, and natural language processing. It is also used in projects such as SciPy, Astropy, OpenCV, and scikit-image. Scikit-learn is a valuable tool for data scientists, machine learning engineers, and software developers who are looking for a comprehensive and versatile library for machine learning and data analysis. (4)
Scikit-learn Tutorials: https://scikit-learn.org/stable/
Acknowledgments:
- Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Buitinck, L., Larsen, R., Niculae, V., Prettenhofer, P., et al. (2011). Scikit-learn: Machine learning in Python. Journal of machine learning research, 12(Oct), 2825-2830.
- Guido, S., & Van Rossum, G. (2020). SciPy: Open source scientific tools for Python. Nature Methods, 17(3), 261-272.
- Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
- Von Luxburg, U. (2016). scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 17(81), 1-7.