scikitlearns

Scikitlearns is a term commonly used to refer to scikit-learn, an open-source machine learning library for Python. Scikit-learn provides simple and efficient tools for data mining and data analysis and is built on the NumPy, SciPy, and matplotlib ecosystems.

The project originated in 2007 as part of the Google Summer of Code and has grown through broad community contributions. It is now maintained by a dedicated developer community and is a core component of the scientific Python ecosystem used in research, teaching, and industry.

Scikit-learn offers a broad range of algorithms for supervised and unsupervised learning, including linear models, logistic regression, decision trees, random forests, support-vector machines, k-means, and principal component analysis. It also provides preprocessing utilities, model evaluation tools, cross-validation, pipelines, and feature extraction workflows to streamline machine learning workflows.

The API centers on estimator objects that implement fit, transform, and predict methods, enabling a uniform interface across different algorithms. It integrates with NumPy arrays and sparse matrices, supports pipelines and hyperparameter tuning via grid search and randomized search, and interoperates with other scientific Python libraries.

Scikit-learn is released under the BSD license and is widely used in academia and industry for teaching, prototyping, and production pipelines. It is not designed for deep learning on large-scale neural networks, where dedicated frameworks such as TensorFlow or PyTorch are typically preferred.