scikit-learn: machine learning in Python — scikit-learn 0.16.1 documentation
Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.
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Website Key Features
Simple and efficient tools for data mining and data analysis
Accessible to everybody, and reusable in various contexts.
Built on NumPy, SciPy, and matplotlib
Integrates well with the scientific Python ecosystem.
Open source, commercially usable - BSD license
Allows for unrestricted use in both academic and commercial settings.
Wide range of supervised and unsupervised learning algorithms
From classical linear models to more advanced techniques like neural networks.
Cross-validation and model selection tools
Helps in evaluating the performance of models and selecting the best one.
Feature extraction and feature selection
Supports transforming raw data into features that can be used by machine learning algorithms.
Dimensionality reduction
Techniques like PCA and t-SNE for reducing the number of random variables under consideration.
Clustering
Grouping unlabeled data into clusters, useful for exploratory data analysis.
Preprocessing and normalization
Tools for scaling, centering, normalization, binarization, and imputation of missing values.
Model persistence
Save and load models using Python’s built-in persistence model, pickle.
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Additional information
Community and Support
Scikit-learn has a large and active community. It offers extensive documentation, user guides, and examples. There are also mailing lists and a GitHub repository for support and contributions.
Performance
While scikit-learn is not the fastest machine learning library, it is optimized for ease of use, clarity, and consistency. For performance-critical applications, it can be combined with libraries like Cython or joblib.
Educational Use
Scikit-learn is widely used in academia for teaching and research due to its simplicity and the breadth of algorithms it covers.
Integration with Other Libraries
Scikit-learn can be integrated with other Python libraries such as Pandas for data manipulation, Matplotlib for plotting, and IPython for interactive computing.
Development and Maintenance
The library is actively developed and maintained by a team of volunteers. It is part of the broader scikit-learn project, which includes other tools for machine learning and data science.
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