A set of python modules for machine learning and data mining
Project description
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.
Website: http://scikit-learn.org
Installation
Dependencies
scikit-learn requires:
Python (>= 2.7 or >= 3.3)
NumPy (>= 1.8.2)
SciPy (>= 0.13.3)
For running the examples Matplotlib >= 1.1.1 is required.
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 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.
Development
We welcome new contributors of all experience levels. The scikit-learn community goals are to be helpful, welcoming, and effective. The Development Guide has detailed information about contributing code, documentation, tests, and more. We’ve included some basic information in this README.
Important links
Official source code repo: https://github.com/scikit-learn/scikit-learn
Download releases: https://pypi.python.org/pypi/scikit-learn
Issue tracker: https://github.com/scikit-learn/scikit-learn/issues
Source code
You can check the latest sources with the command:
git clone https://github.com/scikit-learn/scikit-learn.git
Setting up a development environment
Quick tutorial on how to go about setting up your environment to contribute to scikit-learn: https://github.com/scikit-learn/scikit-learn/blob/master/CONTRIBUTING.md
Testing
After installation, you can launch the test suite from outside the source directory (you will need to have the nose package installed):
nosetests -v sklearn
Under Windows, it is recommended to use the following command (adjust the path to the python.exe program) as using the nosetests.exe program can badly interact with tests that use multiprocessing:
C:\Python34\python.exe -c "import nose; nose.main()" -v sklearn
See the web page http://scikit-learn.org/stable/developers/advanced_installation.html#testing for more information.
Random number generation can be controlled during testing by setting the SKLEARN_SEED environment variable.
Submitting a Pull Request
Before opening a Pull Request, have a look at the full Contributing page to make sure your code complies with our guidelines: http://scikit-learn.org/stable/developers/index.html
Project History
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.
The project is currently maintained by a team of volunteers.
Note: scikit-learn was previously referred to as scikits.learn.
Help and Support
Documentation
HTML documentation (stable release): http://scikit-learn.org
HTML documentation (development version): http://scikit-learn.org/dev/
Communication
Mailing list: https://mail.python.org/mailman/listinfo/scikit-learn
IRC channel: #scikit-learn at webchat.freenode.net
Stack Overflow: http://stackoverflow.com/questions/tagged/scikit-learn
Website: http://scikit-learn.org
Citation
If you use scikit-learn in a scientific publication, we would appreciate citations: http://scikit-learn.org/stable/about.html#citing-scikit-learn
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distributions
Hashes for scikit_learn-0.19.2-cp37-cp37m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | fefba2a43b92f8393366093b60efbe984a72a2b41cce16b4002005e4104ef938 |
|
MD5 | f9e1c88b465a219efdfa8b10508686f9 |
|
BLAKE2b-256 | adaf1288e50e80309d9de6b8ffb44c3cc8c754f1283d124ffa4a9940a96f7539 |
Hashes for scikit_learn-0.19.2-cp37-cp37m-win32.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 75297f3dd6685f01555f1bb75846995d45650af417280b69c81bf11b6987aed5 |
|
MD5 | 95e4651fc05491847058617b133d24fe |
|
BLAKE2b-256 | 4ead84c2349afcf44d5fb142b00716f67f253020a77621b206f98d2f8e5b974c |
Hashes for scikit_learn-0.19.2-cp37-cp37m-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 51d99a08c8bf689cf60c9d8dca6e3d3e5f6d762def85ad735dcea11fb528a89b |
|
MD5 | 1c3e4c4ec8dbfc4ad4b09dbcab2ec10e |
|
BLAKE2b-256 | ebf18ab06a3ae2f047098a953e43e1714f47ae0db28d2960afcacaa04d671b51 |
Hashes for scikit_learn-0.19.2-cp37-cp37m-manylinux1_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 42cba716db197e0d1670e2fc13c4cc4a86d5c5358120ccfee6ec427b154e74ff |
|
MD5 | a8adc73e72c0bde88f336e59bacc3cb6 |
|
BLAKE2b-256 | c3d7b7c0038d33feb79b106d71b251bb5dbf06b009058a43b0c03e17befbc95d |
Hashes for scikit_learn-0.19.2-cp37-cp37m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 47b4090b7686642e41176becb7c42ef3cc665d7ee0db5e7ea5d307ec9779327e |
|
MD5 | 86a19b7468bb41f46f260bd66d3e471b |
|
BLAKE2b-256 | 774e397996d781c98198863478c832f66e8312e6911d8c2de06959f767f26039 |
Hashes for scikit_learn-0.19.2-cp36-cp36m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1725540b754a9967778e9385e1ee2c8db50d5ab70ed835c9f5e36002ffabc169 |
|
MD5 | af2f9520a742c249d31dcad55c642a7e |
|
BLAKE2b-256 | dabf05b08c952a73b6e7ab3700c4d2bc2d466bdfa990ef7f2d4d8a5eaa8d6c0b |
Hashes for scikit_learn-0.19.2-cp36-cp36m-win32.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | c69e5c6051366a6ac9600d730276db939b1a205e42504ec0b8371f154b0058db |
|
MD5 | 1033af234674cbfd2e71923cf647b74d |
|
BLAKE2b-256 | 35e2c3c86b1953d617467c426d05207ad2473a470e0150c15b5b8936a8236332 |
Hashes for scikit_learn-0.19.2-cp36-cp36m-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 42ad71502237c9fe300ecf157f5a394df717789a2dde541dd7034b539c70bdcc |
|
MD5 | e15ce54fdab65e8ddb04ba17d80a6f34 |
|
BLAKE2b-256 | f9c88db4108aba5e2166cd2ea4eafa1a4b82f89240a1fa85733029cc2358ad1f |
Hashes for scikit_learn-0.19.2-cp36-cp36m-manylinux1_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | ad4db28d3dc16c01df75ed6efb72524537de3839a5d179fcf94094359fc72ec5 |
|
MD5 | bbbea071d4e4f6a60ef22939c875d980 |
|
BLAKE2b-256 | cfdbd754f807d02458a6a6947d3c86c72a7093b7df616aa409b0c5b109f203d0 |
Hashes for scikit_learn-0.19.2-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 0a718b5ffbd5053fb3f9e1a2e20b7c4f256dd8035e246b907d3117d20bac0260 |
|
MD5 | 9ecc4d0245f8082deef38cd470797e9c |
|
BLAKE2b-256 | cfdbf6375ee4b604209d88447bffab074f236d5357a4f6fa38901362311ed18d |
Hashes for scikit_learn-0.19.2-cp35-cp35m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3e3ce307d7c5c5811658ba8686b24b571a8244eaafe707665ad601f400d5ce98 |
|
MD5 | 9d9c55569346065388d114c367edddae |
|
BLAKE2b-256 | 32b946431bd1dff91527daecb3e4e8d28993ef3ab357120115a1a3d17b319f37 |
Hashes for scikit_learn-0.19.2-cp35-cp35m-win32.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | f8329ac2160ad8bbbac6a507374685ceca3f24ca427fa9ee61a501280e1972d9 |
|
MD5 | 7980831a107d1e93c619e7a18e7331d1 |
|
BLAKE2b-256 | 4010040f34e1ee23fbb26d81697fa9f51b2535c8bb2914515673175648bdb964 |
Hashes for scikit_learn-0.19.2-cp35-cp35m-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | f6c7bf8cd4de1640b760b47f4d28deb26dbbf9acbe0194cdff54a898e190d872 |
|
MD5 | a26690c39a724e13948ec8178d179815 |
|
BLAKE2b-256 | b6e2a1e254a4a4598588d4fe88b45ab88a226c289ecfd0f6c90474eb6a9ea6b3 |
Hashes for scikit_learn-0.19.2-cp35-cp35m-manylinux1_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | c3d852d49d6c1710089d4513702099fa6f8e1aebfedf222319d80c47b0a195f8 |
|
MD5 | cc571656d470fe291fc885bd8295b051 |
|
BLAKE2b-256 | 7df0f2c3091cf7d096b6c20f8a71867a55aab6d079c4562712a4ceb8cdeb7fa1 |
Hashes for scikit_learn-0.19.2-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b3e4681253e95da5aa5c231889a32b084fd997962bf8beda6f796bf422f734b2 |
|
MD5 | 80f7f6fd1c46d4c47a6150941176c538 |
|
BLAKE2b-256 | b120f13a7a1565b8af66c1e9d7799be9557e40e6cdb541bbf0d014792ab8a69d |
Hashes for scikit_learn-0.19.2-cp34-cp34m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b3dc88c4d2bcb26ffc5afe16d053ae28317d7d1de083651defcd5453a04f1563 |
|
MD5 | 1487e68430b7e1484a3e1c3f0f997d24 |
|
BLAKE2b-256 | 0e80dc650ba50b7440759884eb1ce08c655f88e9bf85d22cc68367f67504937e |
Hashes for scikit_learn-0.19.2-cp34-cp34m-win32.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9ebb38ab1d0ee143982aed561811903ac6c1abb512ae2b9019b3b65bde63ffb9 |
|
MD5 | f6ce9467e1c381549629583bebd581e6 |
|
BLAKE2b-256 | c6824b7f3e10e0924d53606423bf043fbf6dcd4d7e3810f4de5feaed6bec5003 |
Hashes for scikit_learn-0.19.2-cp34-cp34m-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | f1428af5c381f6eef30ffbc7e047b7c713d4efa5d7bf5e57b62b3fc8d387044b |
|
MD5 | 602c71921ca976ad42819b2ec5d9be57 |
|
BLAKE2b-256 | cd359eb69f2540882ce90e2776a3b201b6e59f3aae869df0fb11f33c39c59bd6 |
Hashes for scikit_learn-0.19.2-cp34-cp34m-manylinux1_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | c6612e7e43988b8b5e1957150449493a55f9c059de641083df7a964f86f2d1e7 |
|
MD5 | 5e2882007b9dc3f978757c3f39e12f85 |
|
BLAKE2b-256 | 143e00eea1005eecd8da9633778cc4a62f08bd43018721e0f55e3b27762abb4f |
Hashes for scikit_learn-0.19.2-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | efd842d70b87e3ef3429c3149840b9189d4441ca951ab0cec62c94a964e219d9 |
|
MD5 | d5cefedeba7b2dc864a84ac46d59fdb8 |
|
BLAKE2b-256 | 75c14cf848208933f5254fca8060f722c88957c2ba47be7bb88ce9fa75bfa4ea |
Hashes for scikit_learn-0.19.2-cp27-cp27mu-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | a402c1484fe65df42d5dbc22a58e0695fe3afe2b0b229aee2a09c6d60ba8e5c2 |
|
MD5 | ada4f40d1465bc616cf2ed007b5f6f6e |
|
BLAKE2b-256 | bc67370aa248f54769a56216707ad7b9af19745e85a603fafa47bde353f327fb |
Hashes for scikit_learn-0.19.2-cp27-cp27mu-manylinux1_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5f7577fbb2399a4712e96cf0e786638168940a876c33735a1b5d5a86ba4b1370 |
|
MD5 | 2b31bb7a2492e7c48984580106454764 |
|
BLAKE2b-256 | ee26c033ef145d725513566f8cf9b25e6bdb58e9bae3072a0b93f6fd10b80001 |
Hashes for scikit_learn-0.19.2-cp27-cp27m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | ce121baa8e85ec27c3065281657dcd78adaab7dcb046c7fe96ad4e5a9dcb6610 |
|
MD5 | c37899aeaef465d44ca5d44d4d04b8a0 |
|
BLAKE2b-256 | b29e8f4db9db8b16712cf7984686ce21ee88c6668c670c327280dd5fcccf5555 |
Hashes for scikit_learn-0.19.2-cp27-cp27m-win32.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | ed2a9a9bea6ec443b7effe5695c9c168b7bf9a67df6d880729760feda871b6a3 |
|
MD5 | b441653ffd630ada36081094497ae079 |
|
BLAKE2b-256 | 1579f88871051db5d30e2ae88129802ffa4c3186e7a6ad090b4d2c0c2381cbfd |
Hashes for scikit_learn-0.19.2-cp27-cp27m-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 69a34d389d9ca4687ad00af4e11d53686771f484c37366f68617ef656bab16ab |
|
MD5 | efedf73b7078aa3a02178fb3e5f76335 |
|
BLAKE2b-256 | 95510b4703a6adc12e865514577eff222509553ce1777dbad7ecb051b478c7ef |
Hashes for scikit_learn-0.19.2-cp27-cp27m-manylinux1_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | aad6b9aac1617bd7efa0450643888bbd3410679a94bc8680d9863825686ef369 |
|
MD5 | 004da54f13c1460ea0d46c3e263626ba |
|
BLAKE2b-256 | 3621065f4323182fef90b8558c6c6c557c0b1bfa08b461f2bccb37ac540f159f |
Hashes for scikit_learn-0.19.2-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 66bfc2b6b15db1725d03ea657ec9184ff09dcbf1ecd834ef85f2edc2c9cbba97 |
|
MD5 | 7fbc06726b573f6a92f1940c84f1e8e6 |
|
BLAKE2b-256 | 2247dfc1c815a75afc940789df0e125d55cf9e1eceaf589d89f285f9df575235 |