Scikit-learn is a machine-learning library for Python that provides simple and efficient tools for data analysis and data mining, with a focus on machine learning. It is accessible to everybody and reusable in various contexts. It is built on NumPy and SciPy. The project is open source and commercially usable (BSD license).
scikit-learn is a machine-learning library for Python that provides simple and efficient tools for data analysis and data mining. It is built on NumPy, SciPy, and matplotlib. The project is open source and commercially usable (BSD license).
Resources
- Scikit-learn home page
- Scikit-learn source code on GitHub
- Scikit-learn documentation: stable branch
- Scikit-learn documentation: development branch
- Mailing List
- IRC: #scikit-learn
- Gitter and Gitter dev
Related Libraries
- sklearn-pandas - bridge library between scikit-learn and pandas
- scikit-image - scikit-learn-compatible API for image processing and computer vision for machine learning tasks
- sklearn laboratory - scikit-learn wrapper that enables running larger scikit-learn experiments and feature sets
- sklearn deap - scikit-learn wrapper that enables hyper parameter tuning using evolutionary algorithms instead of gridsearch in scikit-learn
- hyperopt-sklearn - Hyper-parameter optimization for sklearn
- scikit-plot - visualization library for quickly generating common plots in machine learning studies
- sklearn-porter - library for turning trained scikit-learn models into compiled c, java, or javascript code
- sklearn_theano - scikit-learn-compatible objects (estimators, transformers, and datasets) using theano internally
- sparkit-learn - scikit-learn API that uses pyspark's distributed computing model
- joblib - scikit-learn parallelization library
