An introduction to scikit-learn

Since its release in 2007, scikit-learn has become one of the most popular machine learning libraries. scikit-learn provides algorithms for machine learning tasks including classification, regression, dimensionality reduction, and clustering. It also provides modules for pre-processing data, extracting features, optimizing hyperparameters, and evaluating models.

scikit-learn is built on the popular Python libraries NumPy and SciPy. NumPy extends Python to support efficient operations on large arrays and multi-dimensional matrices. SciPy provides modules for scientific computing. The visualization library matplotlib is often used in conjunction with scikit-learn.

scikit-learn is popular for academic research because its API is well-documented, easy-to-use, and versatile. Developers can use scikit-learn to experiment with different algorithms by changing only a few lines of code. scikit-learn wraps some popular implementations of machine learning algorithms, such as LIBSVM and LIBLINEAR. Other Python libraries, including NLTK, include wrappers for scikit-learn. scikit-learn also includes a variety of datasets, allowing developers to focus on algorithms rather than obtaining and cleaning data.

Licensed under the permissive BSD license, scikit-learn can be used in commercial applications without restrictions. Many of scikit-learn's algorithms are fast and scalable to all but massive datasets. Finally, scikit-learn is noted for its reliability; much of the library is covered by automated tests.