A set of python modules for machine learning and data mining

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for adrin from gravatar.com adrin Avatar for GaelVaroquaux from gravatar.com GaelVaroquaux Avatar for glemaitre from gravatar.com glemaitre Avatar for jeremiedbb from gravatar.com jeremiedbb Avatar for jnothman from gravatar.com jnothman Avatar for ogrisel from gravatar.com ogrisel Avatar for t3kcit from gravatar.com t3kcit Avatar for thomasjpfan from gravatar.com thomasjpfan

Unverified details

These details have not been verified by PyPI
Project links
Meta
Classifiers

Project description

Travis AppVeyor Codecov 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 About us page for a list of core contributors.

It is currently maintained by a team of volunteers.

Website: http://scikit-learn.org

Installation

Dependencies

scikit-learn requires:

  • Python (>= 2.7 or >= 3.4)

  • NumPy (>= 1.8.2)

  • SciPy (>= 0.13.3)

Scikit-learn 0.20 is the last version to support Python2.7. Scikit-learn 0.21 and later will require Python 3.5 or newer.

For running the examples Matplotlib >= 1.4 is required. A few examples require scikit-image >= 0.11.3, a few examples require pandas >= 0.17.1 and a few example require joblib >= 0.11.

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.

Changelog

See the changelog for a history of notable changes to scikit-learn.

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 pytest >= 3.3.0 installed):

pytest sklearn

See the web page http://scikit-learn.org/dev/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 About us page for a list of core 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

Verified details

These details have been verified by PyPI
Maintainers
Avatar for adrin from gravatar.com adrin Avatar for GaelVaroquaux from gravatar.com GaelVaroquaux Avatar for glemaitre from gravatar.com glemaitre Avatar for jeremiedbb from gravatar.com jeremiedbb Avatar for jnothman from gravatar.com jnothman Avatar for ogrisel from gravatar.com ogrisel Avatar for t3kcit from gravatar.com t3kcit Avatar for thomasjpfan from gravatar.com thomasjpfan

Unverified details

These details have not been verified by PyPI
Project links
Meta
Classifiers

Release history Release notifications | RSS feed

1.5.2

1.5.1

1.5.0

1.5.0rc1 pre-release

1.4.2

1.4.1.post1

1.4.0

1.4.0rc1 pre-release

1.3.2

1.3.1

1.3.0

1.3.0rc1 pre-release

1.2.2

1.2.1

1.2.0

1.2.0rc1 pre-release

1.1.3

1.1.2

1.1.1

1.1.0

1.0.2

1.0.1

1.0

0.24.2

0.24.1

0.24.0

0.23.2

0.23.1

0.23.0

0.22.2.post1

0.22.2

0.22.1

0.22

0.21.3

0.21.2

0.21.1

0.21.0

This version

0.20.4

0.20.3

0.20.2

0.20.1

0.20.0

0.19.2

0.19.1

0.19.0

0.18.2

0.18.1

0.18

0.17.1

0.17

0.16.1

0.16.0

0.15.2

0.15.1

0.15.0

0.14.1

0.14

0.13.1

0.13

0.12.1

0.12

0.11

0.10

0.9

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-0.20.4.tar.gz (11.7 MB view details)

Uploaded Source

Built Distributions

scikit_learn-0.20.4-cp37-cp37m-win_amd64.whl (4.8 MB view details)

Uploaded CPython 3.7m Windows x86-64

scikit_learn-0.20.4-cp37-cp37m-win32.whl (4.3 MB view details)

Uploaded CPython 3.7m Windows x86

scikit_learn-0.20.4-cp37-cp37m-manylinux1_x86_64.whl (5.4 MB view details)

Uploaded CPython 3.7m

scikit_learn-0.20.4-cp37-cp37m-manylinux1_i686.whl (4.9 MB view details)

Uploaded CPython 3.7m

scikit_learn-0.20.4-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 (7.8 MB view details)

Uploaded CPython 3.7m macOS 10.10+ intel macOS 10.10+ x86-64 macOS 10.6+ intel macOS 10.9+ intel macOS 10.9+ x86-64

scikit_learn-0.20.4-cp36-cp36m-win_amd64.whl (4.8 MB view details)

Uploaded CPython 3.6m Windows x86-64

scikit_learn-0.20.4-cp36-cp36m-win32.whl (4.4 MB view details)

Uploaded CPython 3.6m Windows x86

scikit_learn-0.20.4-cp36-cp36m-manylinux1_x86_64.whl (5.4 MB view details)

Uploaded CPython 3.6m

scikit_learn-0.20.4-cp36-cp36m-manylinux1_i686.whl (4.9 MB view details)

Uploaded CPython 3.6m

scikit_learn-0.20.4-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 (7.9 MB view details)

Uploaded CPython 3.6m macOS 10.10+ intel macOS 10.10+ x86-64 macOS 10.6+ intel macOS 10.9+ intel macOS 10.9+ x86-64

scikit_learn-0.20.4-cp35-cp35m-win_amd64.whl (4.8 MB view details)

Uploaded CPython 3.5m Windows x86-64

scikit_learn-0.20.4-cp35-cp35m-win32.whl (4.3 MB view details)

Uploaded CPython 3.5m Windows x86

scikit_learn-0.20.4-cp35-cp35m-manylinux1_x86_64.whl (5.3 MB view details)

Uploaded CPython 3.5m

scikit_learn-0.20.4-cp35-cp35m-manylinux1_i686.whl (4.9 MB view details)

Uploaded CPython 3.5m

scikit_learn-0.20.4-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 (7.8 MB view details)

Uploaded CPython 3.5m macOS 10.10+ intel macOS 10.10+ x86-64 macOS 10.6+ intel macOS 10.9+ intel macOS 10.9+ x86-64

scikit_learn-0.20.4-cp34-cp34m-win_amd64.whl (4.7 MB view details)

Uploaded CPython 3.4m Windows x86-64

scikit_learn-0.20.4-cp34-cp34m-win32.whl (4.4 MB view details)

Uploaded CPython 3.4m Windows x86

scikit_learn-0.20.4-cp34-cp34m-manylinux1_x86_64.whl (5.4 MB view details)

Uploaded CPython 3.4m

scikit_learn-0.20.4-cp34-cp34m-manylinux1_i686.whl (4.9 MB view details)

Uploaded CPython 3.4m

scikit_learn-0.20.4-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 (8.0 MB view details)

Uploaded CPython 3.4m macOS 10.10+ intel macOS 10.10+ x86-64 macOS 10.6+ intel macOS 10.9+ intel macOS 10.9+ x86-64

scikit_learn-0.20.4-cp27-cp27mu-manylinux1_x86_64.whl (5.5 MB view details)

Uploaded CPython 2.7mu

scikit_learn-0.20.4-cp27-cp27mu-manylinux1_i686.whl (5.0 MB view details)

Uploaded CPython 2.7mu

scikit_learn-0.20.4-cp27-cp27m-win_amd64.whl (4.9 MB view details)

Uploaded CPython 2.7m Windows x86-64

scikit_learn-0.20.4-cp27-cp27m-win32.whl (4.5 MB view details)

Uploaded CPython 2.7m Windows x86

scikit_learn-0.20.4-cp27-cp27m-manylinux1_x86_64.whl (5.5 MB view details)

Uploaded CPython 2.7m

scikit_learn-0.20.4-cp27-cp27m-manylinux1_i686.whl (5.0 MB view details)

Uploaded CPython 2.7m

scikit_learn-0.20.4-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 (8.3 MB view details)

Uploaded CPython 2.7m macOS 10.10+ intel macOS 10.10+ x86-64 macOS 10.6+ intel macOS 10.9+ intel macOS 10.9+ x86-64

File details

Details for the file scikit-learn-0.20.4.tar.gz.

File metadata

  • Download URL: scikit-learn-0.20.4.tar.gz
  • Upload date:
  • Size: 11.7 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.3

File hashes

Hashes for scikit-learn-0.20.4.tar.gz
Algorithm Hash digest
SHA256 dfa8a3f33907614030cdfbc8b6f553dacbecaf09f922244f128af3060a137cfc
MD5 f0c44f397738ea1140a596f74592400e
BLAKE2b-256 20a05ec20d7e460d8ebde9ec5e40cda4339c5f546871c3a5bf9228f615b25618

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-0.20.4-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: scikit_learn-0.20.4-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 4.8 MB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.3

File hashes

Hashes for scikit_learn-0.20.4-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 7d0d17789ccf3fd13a6a605f78d586ec4a9b57f94e0e74ce829bc7e7bb796f78
MD5 6360985a32339067f8d4bc1fb4d7726d
BLAKE2b-256 bcd2256a17cf179681b257f76ac9a60312bd9e0f80e073aedd4d4fff9b2fa2c5

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-0.20.4-cp37-cp37m-win32.whl.

File metadata

  • Download URL: scikit_learn-0.20.4-cp37-cp37m-win32.whl
  • Upload date:
  • Size: 4.3 MB
  • Tags: CPython 3.7m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.3

File hashes

Hashes for scikit_learn-0.20.4-cp37-cp37m-win32.whl
Algorithm Hash digest
SHA256 2c8b999c739f8af014a2f1fb0577b3a98ab20c375c37274bf3c1f6a3f06f56f7
MD5 ed0565d2e554c3357cd1c8c0cb760f0d
BLAKE2b-256 544e706752b10c0da76da79ec04ccd5de700a0bd1be34442f69a76cb88fc9f9b

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-0.20.4-cp37-cp37m-manylinux1_x86_64.whl.

File metadata

  • Download URL: scikit_learn-0.20.4-cp37-cp37m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 5.4 MB
  • Tags: CPython 3.7m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.3

File hashes

Hashes for scikit_learn-0.20.4-cp37-cp37m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 f04adc9ad6bc252b080b0b48cda769fa47ea7013e5640b0cb67d26f2f9a9fcff
MD5 8301e1dc8724141b455eef969e7ccc12
BLAKE2b-256 876f5863f1b27523c5d9f0ae2f3d07828ad383ceab39c79726d2ea4da7f679e7

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-0.20.4-cp37-cp37m-manylinux1_i686.whl.

File metadata

  • Download URL: scikit_learn-0.20.4-cp37-cp37m-manylinux1_i686.whl
  • Upload date:
  • Size: 4.9 MB
  • Tags: CPython 3.7m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.3

File hashes

Hashes for scikit_learn-0.20.4-cp37-cp37m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 ea5843a535da2458216ce87084ed51de6de7331e1b6891df9ea29a238a79bb02
MD5 ece0e093910edf29bb0c91dd9ca11a64
BLAKE2b-256 1b1ec45c2fa5abeccb84fc6065ccef74b49635d0f6e3608a5ccb9a8e16acdf16

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-0.20.4-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.

File metadata

File hashes

Hashes for scikit_learn-0.20.4-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 2d9b4b551569bcb87fb50bc77d5f3ca30e1856b0e987619c7a266d4372990ba6
MD5 807d90e6156310dde0a0b055dcf1ea3c
BLAKE2b-256 69ee46c86104541f18706642cf9ae32b5f7614b59cd024ec3a13b0c08751eba6

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-0.20.4-cp36-cp36m-win_amd64.whl.

File metadata

  • Download URL: scikit_learn-0.20.4-cp36-cp36m-win_amd64.whl
  • Upload date:
  • Size: 4.8 MB
  • Tags: CPython 3.6m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.3

File hashes

Hashes for scikit_learn-0.20.4-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 5ede3783e81af866c3d3efbf3d255a88aaebde7d8cb39e6d5c47f9ed5359172b
MD5 9df024f0da3e9493e1327aef04397667
BLAKE2b-256 9beec3c396a0a1233a13a7b5e945c082a6b0011cb3a5153e5e950642dbcd57d4

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-0.20.4-cp36-cp36m-win32.whl.

File metadata

  • Download URL: scikit_learn-0.20.4-cp36-cp36m-win32.whl
  • Upload date:
  • Size: 4.4 MB
  • Tags: CPython 3.6m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.3

File hashes

Hashes for scikit_learn-0.20.4-cp36-cp36m-win32.whl
Algorithm Hash digest
SHA256 73b3da63d35b922fdae378c582326e887d6bdf9282050ef3587d2f96c926bbcd
MD5 9c0441ed1a15f731b12683527b2653de
BLAKE2b-256 629a227c4673fe7ac0f69164ee8258055f470c68abae15914d592b5e3a028005

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-0.20.4-cp36-cp36m-manylinux1_x86_64.whl.

File metadata

  • Download URL: scikit_learn-0.20.4-cp36-cp36m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 5.4 MB
  • Tags: CPython 3.6m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.3

File hashes

Hashes for scikit_learn-0.20.4-cp36-cp36m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 994e807f09c8517ad703e5685599ef195cc90e693e203894a66000dc89e1382f
MD5 ae8ca912b6c96be72438121b5f077c51
BLAKE2b-256 965b5da31a6572dc6b7b2846a7cfcbe2e060a0e6af0e1059a6516965e40371b7

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-0.20.4-cp36-cp36m-manylinux1_i686.whl.

File metadata

  • Download URL: scikit_learn-0.20.4-cp36-cp36m-manylinux1_i686.whl
  • Upload date:
  • Size: 4.9 MB
  • Tags: CPython 3.6m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.3

File hashes

Hashes for scikit_learn-0.20.4-cp36-cp36m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 54e6b20c8392d2047470f504822e8141a726949f2c196c1711fef873650f301d
MD5 8eb7fdea270b37dc8cb61178c8ae8166
BLAKE2b-256 d279166d8aaf60aac8fbb8231e2eb75bd0f77615a5db1dd3cf4c155f90018f87

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-0.20.4-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-0.20.4-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 2fd692310890a6137dd8e550496bfb71ef37a2b8d1a89b87418ed25fe454dde4
MD5 463043ed7d4b68afc800197215809137
BLAKE2b-256 cd498c6900ae088114dca01d45403cd5eec100b09b3400c3d2a582909b195d77

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-0.20.4-cp35-cp35m-win_amd64.whl.

File metadata

  • Download URL: scikit_learn-0.20.4-cp35-cp35m-win_amd64.whl
  • Upload date:
  • Size: 4.8 MB
  • Tags: CPython 3.5m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.3

File hashes

Hashes for scikit_learn-0.20.4-cp35-cp35m-win_amd64.whl
Algorithm Hash digest
SHA256 c42b27bd051af258cb46b7773e031aa52e49e1cc86b769895ca031c35b15a392
MD5 32a585a3e318ea4f22bb1a768a602590
BLAKE2b-256 4e46862d30dc129b5e9a7593ac677bd1c49aced7a6f3d220daf7650b0728126a

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-0.20.4-cp35-cp35m-win32.whl.

File metadata

  • Download URL: scikit_learn-0.20.4-cp35-cp35m-win32.whl
  • Upload date:
  • Size: 4.3 MB
  • Tags: CPython 3.5m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.3

File hashes

Hashes for scikit_learn-0.20.4-cp35-cp35m-win32.whl
Algorithm Hash digest
SHA256 ea42e28bc9f5a3110e77c773a55b3d2b7133ce05c6940cc28a3a554aebd6ab80
MD5 ab82f82cc699495911f2eeea6766f259
BLAKE2b-256 c49e9dcb924e8a6b1049cebd8d6e0c3401b0816baeaf3a9cba1fdef2ab656523

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-0.20.4-cp35-cp35m-manylinux1_x86_64.whl.

File metadata

  • Download URL: scikit_learn-0.20.4-cp35-cp35m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 5.3 MB
  • Tags: CPython 3.5m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.3

File hashes

Hashes for scikit_learn-0.20.4-cp35-cp35m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 59b382e02e132b5079d4520f2c9fec8147d1743f0f11bc6e21907906ef23fa29
MD5 f3df54c215d5319b293691739437d2d1
BLAKE2b-256 9847090f476f77e32bdaca911ab1151eda4dc19e0f8250c8e0c6b430fbe8feaa

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-0.20.4-cp35-cp35m-manylinux1_i686.whl.

File metadata

  • Download URL: scikit_learn-0.20.4-cp35-cp35m-manylinux1_i686.whl
  • Upload date:
  • Size: 4.9 MB
  • Tags: CPython 3.5m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.3

File hashes

Hashes for scikit_learn-0.20.4-cp35-cp35m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 0863989d48ae51ba8803b1daeb936d44f260ee0174f67eee58d6d74280fe6e93
MD5 2690726ea70908f51183bcb1934fd6b3
BLAKE2b-256 a807e89bcd12ef10be9dde4d3c23d7c7b5b19c7c24b6bc3ee59f60f181f8f714

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-0.20.4-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-0.20.4-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 f9553369f246721813f8f6784a40cc39c84ca2c6230272c7c1ec24318aa0a2f9
MD5 71112ce1320d2321c7e2f2575c074056
BLAKE2b-256 fba1267bb74bf4d197a7a356efd95e5477ad55ed42890cbc904540c52229bb90

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-0.20.4-cp34-cp34m-win_amd64.whl.

File metadata

  • Download URL: scikit_learn-0.20.4-cp34-cp34m-win_amd64.whl
  • Upload date:
  • Size: 4.7 MB
  • Tags: CPython 3.4m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.3

File hashes

Hashes for scikit_learn-0.20.4-cp34-cp34m-win_amd64.whl
Algorithm Hash digest
SHA256 03c721eaed97f4f6b92d1247c64639bb1feea0be968f708b56f9dfb836b9d9ca
MD5 22be5c6453694c2ceda155753892da53
BLAKE2b-256 b9d6cb48af080cd703383c8758098b4b4f303e135626644d7e16acc4037cb10a

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-0.20.4-cp34-cp34m-win32.whl.

File metadata

  • Download URL: scikit_learn-0.20.4-cp34-cp34m-win32.whl
  • Upload date:
  • Size: 4.4 MB
  • Tags: CPython 3.4m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.3

File hashes

Hashes for scikit_learn-0.20.4-cp34-cp34m-win32.whl
Algorithm Hash digest
SHA256 7dbb4c23f2911e81fe607c26f1a1277c6f2798192765a9472c33570f8a843b6b
MD5 06b6990c54c4579cfd8dbc8494a87d63
BLAKE2b-256 1f5d66831a07dc80ed26959556ceb7c274a4cc572712e69eb4df32c05be5197a

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-0.20.4-cp34-cp34m-manylinux1_x86_64.whl.

File metadata

  • Download URL: scikit_learn-0.20.4-cp34-cp34m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 5.4 MB
  • Tags: CPython 3.4m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.3

File hashes

Hashes for scikit_learn-0.20.4-cp34-cp34m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 25285960a093781b10d7d26484f8e3523b9d23676329862ba66a753bb0b2e632
MD5 83fb92e80430a1f5bf5bb15c1d749704
BLAKE2b-256 15a70af8f0b92a0657b91283f4dff5c9d36e127d46228d5a1b3ab8d4d74205cd

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-0.20.4-cp34-cp34m-manylinux1_i686.whl.

File metadata

  • Download URL: scikit_learn-0.20.4-cp34-cp34m-manylinux1_i686.whl
  • Upload date:
  • Size: 4.9 MB
  • Tags: CPython 3.4m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.3

File hashes

Hashes for scikit_learn-0.20.4-cp34-cp34m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 3323640ce4ade456f13c4fec97bdeb6a380e6f7005ec6709afb9e554a0b73b02
MD5 4e1c7d484f7d3b3f31d45311a9ddaf0c
BLAKE2b-256 d1ff90ff964c2033b2170fb929808603149ba39d3bf1fe609e2f69348bfaa350

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-0.20.4-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-0.20.4-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 e380c086e8cae1480365068e57469302a1ee8fae98eb02010c80ef2cb292f7a3
MD5 16cf61f667ca7bdf75c246e87218f6cc
BLAKE2b-256 18e8a4410daf8648996e18b8cde81dd41652b759fee66aac7fe52f0fcebbf957

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-0.20.4-cp27-cp27mu-manylinux1_x86_64.whl.

File metadata

  • Download URL: scikit_learn-0.20.4-cp27-cp27mu-manylinux1_x86_64.whl
  • Upload date:
  • Size: 5.5 MB
  • Tags: CPython 2.7mu
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.3

File hashes

Hashes for scikit_learn-0.20.4-cp27-cp27mu-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 4de3f1e35c29dac3b1f8afaa6afe1b682dbb19159c5ee5d77f97fa5ceb1d020d
MD5 1cdd8caa9a829cc77ef309dc70f4240d
BLAKE2b-256 319f042db462417451e81035c3d43b722e88450c628a33dfda69777a801b0d40

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-0.20.4-cp27-cp27mu-manylinux1_i686.whl.

File metadata

  • Download URL: scikit_learn-0.20.4-cp27-cp27mu-manylinux1_i686.whl
  • Upload date:
  • Size: 5.0 MB
  • Tags: CPython 2.7mu
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.3

File hashes

Hashes for scikit_learn-0.20.4-cp27-cp27mu-manylinux1_i686.whl
Algorithm Hash digest
SHA256 9afeffd7fba365eb8d0603b13d4ea76ee63cacded21556c65e98b4682c2b09bf
MD5 3063b4b7b896904eb52d66bcbc925315
BLAKE2b-256 9ce4cecf1c8ee8baf82f0bd3de20e92e88df71a6d5abd15d796ca4977e7d4e31

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-0.20.4-cp27-cp27m-win_amd64.whl.

File metadata

  • Download URL: scikit_learn-0.20.4-cp27-cp27m-win_amd64.whl
  • Upload date:
  • Size: 4.9 MB
  • Tags: CPython 2.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.3

File hashes

Hashes for scikit_learn-0.20.4-cp27-cp27m-win_amd64.whl
Algorithm Hash digest
SHA256 b47d865ae832f0d1538f449b0a42cf37121bbcd514eb1d37c0ff0b70b0ac192c
MD5 e1e46d56f68369018bca18d1c7a94a05
BLAKE2b-256 92ea82a9ae87428ce6fb0956c89b1947cc6a70f6c6548b1b2b9da34c4511fe0d

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-0.20.4-cp27-cp27m-win32.whl.

File metadata

  • Download URL: scikit_learn-0.20.4-cp27-cp27m-win32.whl
  • Upload date:
  • Size: 4.5 MB
  • Tags: CPython 2.7m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.3

File hashes

Hashes for scikit_learn-0.20.4-cp27-cp27m-win32.whl
Algorithm Hash digest
SHA256 3b95e4a693d1d1e91ebc22abcc4e3731f9bfd2be2308b071b19db46a7d0fa6f6
MD5 4c5c848f5773393ccb65dbc5649cd1c2
BLAKE2b-256 d675dec5a3749e605f25c7cb0d22f73efea20cab3582b9e645c10ed88ab0ceb3

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-0.20.4-cp27-cp27m-manylinux1_x86_64.whl.

File metadata

  • Download URL: scikit_learn-0.20.4-cp27-cp27m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 5.5 MB
  • Tags: CPython 2.7m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.3

File hashes

Hashes for scikit_learn-0.20.4-cp27-cp27m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 546a2bf74c07634f65dc8e92ea50c124eb2adde10415cf25a43cf8c0a79671b7
MD5 2cec8d730d13d9e36f493216f47253ff
BLAKE2b-256 9f57a437dcc2b05f77fb75c00098c35131e2beb5101648b3586e9c9355ecd227

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-0.20.4-cp27-cp27m-manylinux1_i686.whl.

File metadata

  • Download URL: scikit_learn-0.20.4-cp27-cp27m-manylinux1_i686.whl
  • Upload date:
  • Size: 5.0 MB
  • Tags: CPython 2.7m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.3

File hashes

Hashes for scikit_learn-0.20.4-cp27-cp27m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 43a521c6992adf28a78abdb2d18b77c481a7dd86f7bd1de8f3e02231a88904cc
MD5 1b568e0ae932be4caadc3741f5673562
BLAKE2b-256 53706e3f104287326c804e7aabbba13d36a62387f76de90cd96b0b010c360355

See more details on using hashes here.

Provenance

File details

Details for the file scikit_learn-0.20.4-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-0.20.4-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 d9019683ec2382e686ecca5377b7c99f059a9b3a7f6a0bf6afddb10aec82b016
MD5 3336e6eb8afc4e123f6ce22705ba14bc
BLAKE2b-256 19af1e116d24d6d74da12d90c42f408f16dae8f1a59ab4d95a48acbd2c277183

See more details on using hashes here.

Provenance

Supported by

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