scikit-learn is an open source Python module for machine learning built on top of SciPy. It offers efficient versions of a large number of common algorithms. The software displays a clean, uniform, and streamlined API, with good online documentation.
The software provides 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.
scikit-learn is largely written in Python, with some core algorithms written in Cython to optimise performance.
scikit-learn is one of the most useful modules for machine learning in Python.
The software has the following dependencies:
- Python (>= 2.7 or >= 3.4)
- NumPy (>= 1.8.2)
- SciPy (>= 0.13.3)
scikit-learn also uses CBLAS, the C interface to the Basic Linear Algebra Subprograms library, and matplotlib to generate the supplied examples.
- Simple and efficient tools for data mining and data analysis.
- Supervised learning algorithms – great coverage for this type of algorithm. There’s generalised Linear Modules, Linear and Quadratic Discriminant Analysis, Support Vector Machines, Decision Trees, Bayesian methods, Gaussian Processes, Neural network models, and more.
- Cross-validation – various methods to check the accuracy of supervised models on unseen data.
- Unsupervised learning algorithms – again, a wide range of algorithms are available, including Gaussian mixture modules, manifold learning, clustering, factor analysis, covariance estimation, density estimation, and more.
- Dataset transformations – provides a library of transformers, which clean, reduce, expand, or generate feature representations.
- Feature extraction – useful for extracting features from images and text.
- Various sample datasets – the datasets are useful in learning how to use scikit-learn. They include: Boston house prices dataset, iris dataset, diabetes dataset, digits dataset, linnerud dataset, wine dataset, and a breast cancer dataset.
- Built on NumPy, SciPy, and matplotlib.
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