Scikit-learn

Scikit-learn is an open source machine learning library for Python. It is built on top of the popular Python scientific computing library NumPy and is designed to be easy to use and efficient.

Scikit-learn is intended to provide a straightforward and simple set of tools for data mining and data analysis. It incorporates methods from a variety of disciplines ranging from supervised learning, unsupervised learning, and semi-supervised learning. It also provides a consistent API, making it easy to switch from one algorithm to another with minimal refactoring of code.

The library is built upon three major foundations: data integration, feature engineering, and model selection. It makes it easy to process data from a variety of sources, including flat files, databases, and remote URLs. The feature engineering part facilitates a wide range of feature selection and extraction techniques. It also provides support for a variety of supervised and unsupervised learning algorithms, including support vector machine, decision trees, and many more.

Scikit-learn provides two essential building blocks for building machine learning applications. They are model selection and model evaluation. The model selection part helps identify the most suitable classifier algorithm to use. It also contains a variety of performance metrics for evaluating the selected models. Similarly, the model evaluation module includes a wide range of performance metrics to evaluate the models’ accuracy.

As a popular, cross-platform machine learning library, Scikit-learn is widely adopted by data science practitioners and machine learning developers. It is actively maintained with a steady stream of bug fixes and feature updates. All the functions and algorithms of Scikit-learn are licenced under the BSD 3-clause license.