Skip to main content

A set of python modules for machine learning and data mining

Project description

Travis AppVeyor Coveralls CircleCI Python27 Python35 PyPi DOI

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 (>= 2.6 or >= 3.3)

  • NumPy (>= 1.6.1)

  • SciPy (>= 0.9)

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.

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/install.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

Communication

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


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

scikit-learn-0.18.2.tar.gz (9.2 MB view details)

Uploaded Source

Built Distributions

scikit_learn-0.18.2-cp36-cp36m-win_amd64.whl (4.1 MB view details)

Uploaded CPython 3.6mWindows x86-64

scikit_learn-0.18.2-cp36-cp36m-win32.whl (3.7 MB view details)

Uploaded CPython 3.6mWindows x86

scikit_learn-0.18.2-cp36-cp36m-manylinux1_x86_64.whl (11.8 MB view details)

Uploaded CPython 3.6m

scikit_learn-0.18.2-cp36-cp36m-manylinux1_i686.whl (11.1 MB view details)

Uploaded CPython 3.6m

scikit_learn-0.18.2-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 (7.2 MB view details)

Uploaded CPython 3.6mmacOS 10.10+ Intel (x86-64, i386)macOS 10.10+ x86-64macOS 10.6+ Intel (x86-64, i386)macOS 10.9+ Intel (x86-64, i386)macOS 10.9+ x86-64

scikit_learn-0.18.2-cp35-cp35m-win_amd64.whl (4.1 MB view details)

Uploaded CPython 3.5mWindows x86-64

scikit_learn-0.18.2-cp35-cp35m-win32.whl (3.7 MB view details)

Uploaded CPython 3.5mWindows x86

scikit_learn-0.18.2-cp35-cp35m-manylinux1_x86_64.whl (11.7 MB view details)

Uploaded CPython 3.5m

scikit_learn-0.18.2-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 (7.2 MB view details)

Uploaded CPython 3.5mmacOS 10.10+ Intel (x86-64, i386)macOS 10.10+ x86-64macOS 10.6+ Intel (x86-64, i386)macOS 10.9+ Intel (x86-64, i386)macOS 10.9+ x86-64

scikit_learn-0.18.2-cp34-cp34m-win_amd64.whl (4.1 MB view details)

Uploaded CPython 3.4mWindows x86-64

scikit_learn-0.18.2-cp34-cp34m-win32.whl (3.8 MB view details)

Uploaded CPython 3.4mWindows x86

scikit_learn-0.18.2-cp34-cp34m-manylinux1_x86_64.whl (11.9 MB view details)

Uploaded CPython 3.4m

scikit_learn-0.18.2-cp34-cp34m-manylinux1_i686.whl (11.1 MB view details)

Uploaded CPython 3.4m

scikit_learn-0.18.2-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 (7.2 MB view details)

Uploaded CPython 3.4mmacOS 10.10+ Intel (x86-64, i386)macOS 10.10+ x86-64macOS 10.6+ Intel (x86-64, i386)macOS 10.9+ Intel (x86-64, i386)macOS 10.9+ x86-64

scikit_learn-0.18.2-cp33-cp33m-manylinux1_x86_64.whl (11.3 MB view details)

Uploaded CPython 3.3m

scikit_learn-0.18.2-cp33-cp33m-manylinux1_i686.whl (10.5 MB view details)

Uploaded CPython 3.3m

scikit_learn-0.18.2-cp27-cp27mu-manylinux1_x86_64.whl (11.6 MB view details)

Uploaded CPython 2.7mu

scikit_learn-0.18.2-cp27-cp27mu-manylinux1_i686.whl (10.9 MB view details)

Uploaded CPython 2.7mu

scikit_learn-0.18.2-cp27-cp27m-win_amd64.whl (4.3 MB view details)

Uploaded CPython 2.7mWindows x86-64

scikit_learn-0.18.2-cp27-cp27m-win32.whl (3.9 MB view details)

Uploaded CPython 2.7mWindows x86

scikit_learn-0.18.2-cp27-cp27m-manylinux1_x86_64.whl (11.7 MB view details)

Uploaded CPython 2.7m

scikit_learn-0.18.2-cp27-cp27m-manylinux1_i686.whl (10.9 MB view details)

Uploaded CPython 2.7m

scikit_learn-0.18.2-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 (7.5 MB view details)

Uploaded CPython 2.7mmacOS 10.10+ Intel (x86-64, i386)macOS 10.10+ x86-64macOS 10.6+ Intel (x86-64, i386)macOS 10.9+ Intel (x86-64, i386)macOS 10.9+ x86-64

scikit-learn-0.18.2.win-amd64-py3.5.exe (4.7 MB view details)

Uploaded Source

scikit-learn-0.18.2.win-amd64-py2.7.exe (4.5 MB view details)

Uploaded Source

scikit-learn-0.18.2.win32-py3.5.exe (4.2 MB view details)

Uploaded Source

scikit-learn-0.18.2.win32-py2.7.exe (4.1 MB view details)

Uploaded Source

File details

Details for the file scikit-learn-0.18.2.tar.gz.

File metadata

  • Download URL: scikit-learn-0.18.2.tar.gz
  • Upload date:
  • Size: 9.2 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for scikit-learn-0.18.2.tar.gz
Algorithm Hash digest
SHA256 f78c3e11bf38838eaf637cdd9e8d6b575a4a4048d1670a03a72b0d00d3f39ffa
MD5 3e464c7e218f4a4f4ba68e5842a7752f
BLAKE2b-256 26c221c612f3a1b1ba97b7b4bbd1fcdc59b475a09e25efad13fec4565ab9d563

See more details on using hashes here.

File details

Details for the file scikit_learn-0.18.2-cp36-cp36m-win_amd64.whl.

File metadata

File hashes

Hashes for scikit_learn-0.18.2-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 9c1194d25cd8a68fabde6fe5fb0ce0d9ade78a1785d01731a0dc75e0ad66302c
MD5 cd22c02b73d3bdfcc119925427943616
BLAKE2b-256 a786fab0d7835e30a861ed8e35e38e64d071a51f683058a6a70ff5ebd619b9dc

See more details on using hashes here.

File details

Details for the file scikit_learn-0.18.2-cp36-cp36m-win32.whl.

File metadata

File hashes

Hashes for scikit_learn-0.18.2-cp36-cp36m-win32.whl
Algorithm Hash digest
SHA256 cb133084b8530de49bad852bd11e7f613e220fee50ccb6e62add675d1acab433
MD5 c0aaeb582db9dd976760ae9923660b4d
BLAKE2b-256 7d58e2c6c24c12819745b1172439e85ced05cb7bd688ea16ecd4e7c605d9d693

See more details on using hashes here.

File details

Details for the file scikit_learn-0.18.2-cp36-cp36m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for scikit_learn-0.18.2-cp36-cp36m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 31789ed71ddb4facf7b47d6396da7d5485f434ff499ad09e061d67c6b292ff4b
MD5 c7b5153342af0c3b422f02376e1ef423
BLAKE2b-256 d151ca5be39c576c981cf8b8359fcb1ee49ca43d59b833415205cec5ef5c0fcd

See more details on using hashes here.

File details

Details for the file scikit_learn-0.18.2-cp36-cp36m-manylinux1_i686.whl.

File metadata

File hashes

Hashes for scikit_learn-0.18.2-cp36-cp36m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 5cbd838cb35a469e634d4d1014db35f06cc4cf1c85022a1b778ecedf0fa37263
MD5 c7b1cc65393aa12c40bf86369777dff6
BLAKE2b-256 06dc7d012e7e5adc81adcc8c8e46c2e5a8237409ac33dc3e08bf22fc882977b1

See more details on using hashes here.

File details

Details for the file scikit_learn-0.18.2-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.

File metadata

File hashes

Hashes for scikit_learn-0.18.2-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 b8c1dbf7c91b4a75096daa4f383219bcc7d74689407ba31bef3ac45e991bfa77
MD5 ad7ac08c9c6bdeba41835645c8f80336
BLAKE2b-256 67a52e42bff2a196a96ccc275aef2b8569d169ad4ff0169a768c1d23c332ef40

See more details on using hashes here.

File details

Details for the file scikit_learn-0.18.2-cp35-cp35m-win_amd64.whl.

File metadata

File hashes

Hashes for scikit_learn-0.18.2-cp35-cp35m-win_amd64.whl
Algorithm Hash digest
SHA256 6b1cf381d3747df84dcf8414dd80a66c975365aa8b0bae6062346bd38d931047
MD5 a141458ea940a53086becd47fdc2e1ec
BLAKE2b-256 d0d104cc8f7807f552e9ce5b1db29597b21c5dd81be796f2983ab82072d137a3

See more details on using hashes here.

File details

Details for the file scikit_learn-0.18.2-cp35-cp35m-win32.whl.

File metadata

File hashes

Hashes for scikit_learn-0.18.2-cp35-cp35m-win32.whl
Algorithm Hash digest
SHA256 37e86626a43791f8433dbd81b21928bd1c2402b226dd86462a562dd97d86100b
MD5 14300af8bcb53a75ed3b13a96a3e71dc
BLAKE2b-256 bc7cfab86d70c5ae3908182ec910b43b8e98c551609cbd0f79f073fe0f2591e3

See more details on using hashes here.

File details

Details for the file scikit_learn-0.18.2-cp35-cp35m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for scikit_learn-0.18.2-cp35-cp35m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 be9400f27a2cf9d10a249217289b6dfc17156800f5b76a7f94b9251ade258d00
MD5 a55e4ac6d034f95c8651e360522abeb0
BLAKE2b-256 7e20e04407935a2520ed569a94cbb3d63ac3b75bc1c33efceb5a5316e5083fec

See more details on using hashes here.

File details

Details for the file scikit_learn-0.18.2-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.

File metadata

File hashes

Hashes for scikit_learn-0.18.2-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 85cb700dc20321b82031159409d02dc7bd9bd84959f61a2466f85921376cccbf
MD5 0b1ed5b0a6d72ffc0f9cc26457493703
BLAKE2b-256 9d308a71eda2f186a2e248b95e5654f9721bb4a9f03646e7c46372147a5e0844

See more details on using hashes here.

File details

Details for the file scikit_learn-0.18.2-cp34-cp34m-win_amd64.whl.

File metadata

File hashes

Hashes for scikit_learn-0.18.2-cp34-cp34m-win_amd64.whl
Algorithm Hash digest
SHA256 01c0b1e8572748dc3200f322ba6a1b1b5f246f51abeebdbcb070e6c69fbf5582
MD5 9d02d418ac32c4f524b25f050e0d285f
BLAKE2b-256 d602ecac2a9e787270398dd5f6795c74dd6613b90bcc5485141b3d016fc5e34e

See more details on using hashes here.

File details

Details for the file scikit_learn-0.18.2-cp34-cp34m-win32.whl.

File metadata

File hashes

Hashes for scikit_learn-0.18.2-cp34-cp34m-win32.whl
Algorithm Hash digest
SHA256 61475fda4f351fb5e1b1a5c78904a0a6c43b11031df66c9bee6969e056d71d91
MD5 6f4aa531e2187e44c7e3ddbdeee68946
BLAKE2b-256 8b97d772f9828ffa679b519c1163d818ccb95f4f6baa5f62df225842c7101b5a

See more details on using hashes here.

File details

Details for the file scikit_learn-0.18.2-cp34-cp34m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for scikit_learn-0.18.2-cp34-cp34m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 06da7592ecee71d92495e975d4e4b03ca8848834bd170ba4663c19d7026ad763
MD5 0ff17e8f2df29ccb74de68afe4e64866
BLAKE2b-256 46c694089c7ef22bb5df7d797c9ca9a7dbce1cea881e42a912a77adc1f620f7f

See more details on using hashes here.

File details

Details for the file scikit_learn-0.18.2-cp34-cp34m-manylinux1_i686.whl.

File metadata

File hashes

Hashes for scikit_learn-0.18.2-cp34-cp34m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 28d240d8b57d72ad49ef65eea62237bba26b16209b121b23e8a8c6170ef89aff
MD5 19f19020fc4ae8b5d9f937cc8ddb960a
BLAKE2b-256 8ece004ffeef2947723544a9fb6f15d9c6121eb8afe2f7f7619b6b05bfe138bd

See more details on using hashes here.

File details

Details for the file scikit_learn-0.18.2-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.

File metadata

File hashes

Hashes for scikit_learn-0.18.2-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 687d9029c56ca6f7eb6ac8449f098d0964c2d698dcd80bfa9bf0715cb68264c3
MD5 9cc6aedefca9bff2d4920dc9e2509ce3
BLAKE2b-256 55e705938f756776b75e83c9183fa6c7f46bbe540f0eb3d4e963a7e111135e47

See more details on using hashes here.

File details

Details for the file scikit_learn-0.18.2-cp33-cp33m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for scikit_learn-0.18.2-cp33-cp33m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 c8e12e06d8fb00d51aa901b8c5eb7d56bc5b2e1d44c5767a1d737e03c1a74ebb
MD5 2d681a0e2141cfc53c45a6b806655916
BLAKE2b-256 f158268444c1d45d94ac6d1fa407271032f014dd503229ffc86234f21c929754

See more details on using hashes here.

File details

Details for the file scikit_learn-0.18.2-cp33-cp33m-manylinux1_i686.whl.

File metadata

File hashes

Hashes for scikit_learn-0.18.2-cp33-cp33m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 937369f471c58dd89290436e4efcdec028c3b0931b414fa04f84488e715afdc7
MD5 3934c41bfe733f73e1667a4e100a1cb7
BLAKE2b-256 18cf733d94c8c355d2deb65b7bf6a2a121aabfd55fb815bf38fe8b72177fa0bd

See more details on using hashes here.

File details

Details for the file scikit_learn-0.18.2-cp27-cp27mu-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for scikit_learn-0.18.2-cp27-cp27mu-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 6f8eba1b25fa0290f1925d2cf60aeac9dd18c6031dce7494069c8fec3c5f6d3e
MD5 49082740fba6e786b7e9aa4455c49e9b
BLAKE2b-256 60f0c9db37931e1cf1d7d3a210ac3a18771cbe7ff6375c8f50c256793df63df8

See more details on using hashes here.

File details

Details for the file scikit_learn-0.18.2-cp27-cp27mu-manylinux1_i686.whl.

File metadata

File hashes

Hashes for scikit_learn-0.18.2-cp27-cp27mu-manylinux1_i686.whl
Algorithm Hash digest
SHA256 34db52e3b567727ab6a3a44fb9896d4dfbbcb2077277e288eb3f161bcd134a44
MD5 fbeec42632e47adf7531fbac31b4204c
BLAKE2b-256 094629a710fc4ec9cb2f92a076891c9bb3ad90e2ab87b1de1d6d355dea7ba6bf

See more details on using hashes here.

File details

Details for the file scikit_learn-0.18.2-cp27-cp27m-win_amd64.whl.

File metadata

File hashes

Hashes for scikit_learn-0.18.2-cp27-cp27m-win_amd64.whl
Algorithm Hash digest
SHA256 0e475c00c571b5c6c25fee7192865de60a2adbe11e6e045dfca74bf5faa95b57
MD5 a7caee3757ff8212c64c5aa4fde2c9ab
BLAKE2b-256 ad29e4a07ff80ebaef1dcf02aed8f204e8ae3a86f4c063ebc40e11bd3fb3a7ab

See more details on using hashes here.

File details

Details for the file scikit_learn-0.18.2-cp27-cp27m-win32.whl.

File metadata

File hashes

Hashes for scikit_learn-0.18.2-cp27-cp27m-win32.whl
Algorithm Hash digest
SHA256 2025da5a62f024a377f4f691d5482f2c9a02446d105f6e04f7e8d7c887ef5f2f
MD5 926980c245638982fc1340dc1a1db4dc
BLAKE2b-256 8e94b005c3bc9a98095363e4a2197319fc969a1c4a181da19c8dcc3188e3d4ca

See more details on using hashes here.

File details

Details for the file scikit_learn-0.18.2-cp27-cp27m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for scikit_learn-0.18.2-cp27-cp27m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 4179810495a38bfe32fe8b13fe3b5495a8df537b1494cd34bfed31222ab73542
MD5 6c1fa4f508ae90674035d928dfd56227
BLAKE2b-256 254fe347b798d2d70189c58390ea689b803aa1b666b557be74a2e20190aa2bf8

See more details on using hashes here.

File details

Details for the file scikit_learn-0.18.2-cp27-cp27m-manylinux1_i686.whl.

File metadata

File hashes

Hashes for scikit_learn-0.18.2-cp27-cp27m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 05f1f98fb594cfdb1bb9d18bdf605078fbb646a9b307728e0e016e80493f7f48
MD5 ad0d119f59a5a28de91bf7938dd646cc
BLAKE2b-256 36eee63fac03909a5f2c831a03912db24fb9284acca4dd322a170aac1fdc4a5d

See more details on using hashes here.

File details

Details for the file scikit_learn-0.18.2-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.

File metadata

File hashes

Hashes for scikit_learn-0.18.2-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 6fb1cee93a13564cf2675d03ed0fc92324758c60267d8ac2aacfcb5e1a097b53
MD5 dfcdb6d9061673d5753ec1139fee4ab0
BLAKE2b-256 69e67dbf430583a3128c3e4921c7db54c2c5242339fed045c70aec953d66c4de

See more details on using hashes here.

File details

Details for the file scikit-learn-0.18.2.win-amd64-py3.5.exe.

File metadata

File hashes

Hashes for scikit-learn-0.18.2.win-amd64-py3.5.exe
Algorithm Hash digest
SHA256 b1546dbb87f2756a72d872826c7e5de4bc505fb2c9e69eb7cdd66b41eba05915
MD5 a1423e7a859b55abf6ae7fab455d28f0
BLAKE2b-256 68767b9cad0dae2b5af627d6042f795589bf948cffdac58b0941919a3e31bfe7

See more details on using hashes here.

File details

Details for the file scikit-learn-0.18.2.win-amd64-py2.7.exe.

File metadata

File hashes

Hashes for scikit-learn-0.18.2.win-amd64-py2.7.exe
Algorithm Hash digest
SHA256 0c7bc01b54a714352f4944cdf014091ff136ec2b35e63e2ae31d54e0daa02f0d
MD5 d3a61df2f848990280e1d95eb1887879
BLAKE2b-256 955547a28f715317668e978eaf18b9b0fbefe9a3a894e3647d923260a4726c5a

See more details on using hashes here.

File details

Details for the file scikit-learn-0.18.2.win32-py3.5.exe.

File metadata

File hashes

Hashes for scikit-learn-0.18.2.win32-py3.5.exe
Algorithm Hash digest
SHA256 896c67d1c6cb21f17e2d8722a6b925e530b310ae8178d29e2414425cbe0fda7c
MD5 9d266db4906dfdcdca3ca7879174c1a6
BLAKE2b-256 0041bf7ed3a4287539b6fbba0fbc967380ba94eb4c5d43e6417b084982e4d3bf

See more details on using hashes here.

File details

Details for the file scikit-learn-0.18.2.win32-py2.7.exe.

File metadata

File hashes

Hashes for scikit-learn-0.18.2.win32-py2.7.exe
Algorithm Hash digest
SHA256 eb504a00755194e8274ffd7c1b2130d8da6c4e267f5f65bba36bfee9f7f69511
MD5 e576650277c936596e7fc8fc7ade88b1
BLAKE2b-256 6071966b1fce24b55c3ff6d91db12af2a4b4c1a1810726f98aa55afe1dcd102e

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page