Scikit-learn is not a new tool (it is approaching its tenth birthday); what is new is the rate of adoption of machine-learning tools and techniques outside of academia and major tech companies. Providing a robust set of models and a rich set of functionality, Scikit-learn plays an important role in making machine-learning concepts and capabilities more accessible to a broader (and often non-expert) audience.

Scikit-learn is not a new tool (it is approaching its tenth birthday); what is new is the rate of adoption of machine-learning tools and techniques outside of academia and major tech companies. Providing a robust set of models and a rich set of functionality, Scikit-learn plays an important role in making machine-learning concepts and capabilities more accessible to a broader (and often non-expert) audience.

Scikit-learn is an increasingly popular machine-learning library written in Python. It provides a robust set of machine-learning models such as clustering, classification, regression and dimensionality reduction, and a rich set of functionality for companion tasks like model selection, model evaluation and data preparation. Since it is designed to be simple, reusable in various contexts and well documented, we see this tool accessible even to nonexperts to explore the machine-learning space.