/ Machine Learning

The most popular Python library for Machine Learning

The most popular Python library for Machine Learning

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. See the AUTHORS.rst file for a complete list of contributors.

It is currently maintained by a team of volunteers.

Installation

Dependencies


scikit-learn requires:

- Python (>= 2.7 or >= 3.4)
- NumPy (>= 1.8.2)
- SciPy (>= 0.13.3)

For running the examples Matplotlib >= 1.3.1 is required. A few examples
require scikit-image >= 0.9.3 and a few examples require pandas >= 0.13.1.

scikit-learn also uses CBLAS, the C interface to the Basic Linear Algebra
Subprograms library. scikit-learn comes with a reference implementation, but
the system CBLAS will be detected by the build system and used if present.
CBLAS exists in many implementations; see `Linear algebra libraries
<http://scikit-learn.org/stable/modules/computational_performance.html#linear-algebra-libraries>`_
for known issues.

User installation

If you already have a working installation of numpy and scipy,
the easiest way to install scikit-learn is using pip ::

pip install -U scikit-learn

or conda::

conda install scikit-learn

The documentation includes more detailed installation instructions <http://scikit-learn.org/stable/install.html>_.

GitHub

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