scikit-learn-extra

A set of tools for scikit-learn.

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Project description

scikit-learn-extra - A set of useful tools compatible with scikit-learn

scikit-learn-extra is a Python module for machine learning that extends scikit-learn. It includes algorithms that are useful but do not satisfy the scikit-learn inclusion criteria, for instance due to their novelty or lower citation number.

Installation

Dependencies

scikit-learn-extra requires,

Python (>=3.7)
scikit-learn (>=0.24), and its dependencies

Installation

Latest release can be installed with conda,

conda install -c conda-forge scikit-learn-extra

or from PyPi with,

pip install scikit-learn-extra

License

This package is released under the 3-Clause BSD license.

Project details

These details have not been verified by PyPI

Project links

GitHub Statistics

View statistics for this project via Libraries.io, or by using our public dataset on Google BigQuery

Release history Release notifications | RSS feed

This version

0.3.0

Mar 27, 2023

0.2.0

Apr 14, 2021

0.1.0b2 pre-release

Mar 29, 2020

0.1.0b0 pre-release

Mar 29, 2020

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Hashes for scikit_learn_extra-0.3.0-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm	Hash digest
SHA256	`911435d6fb5adbf1883f84f84e5d2cf1ab9e8131da5966097e4294175387e4d4`
MD5	`1bc526bfe2c87fafd17864067907e425`
BLAKE2b-256	`8efd8a7bfc66f61c4b91faa0caceb24af3d1dd6a8e627b05ccf2f9794b24a229`

Hashes for scikit_learn_extra-0.3.0-cp36-cp36m-win_amd64.whl

Hashes for scikit_learn_extra-0.3.0-cp36-cp36m-win_amd64.whl
Algorithm	Hash digest
SHA256	`265113d3280c17261f4598b2d4277a17e1398076cc5330c6d1fc1b6664c15ea8`
MD5	`54019972a0cd58b926c078e263f44bcc`
BLAKE2b-256	`f63d2d7f3e15591fdb439c2c7cf5829671af33ed77937e2484895841901812c0`

Hashes for scikit_learn_extra-0.3.0-cp36-cp36m-win32.whl

Hashes for scikit_learn_extra-0.3.0-cp36-cp36m-win32.whl
Algorithm	Hash digest
SHA256	`589f11733ce0197bd46189f6f9013696b8f43f85fc66935a90a918cb7965ca1b`
MD5	`7e269db62efa6a47e65507c3eeccbc41`
BLAKE2b-256	`13b9fafe1e3fdff2ccb5a48e4db4010c0070446e9ae308c06f579aec9e654f66`

Hashes for scikit_learn_extra-0.3.0-cp36-cp36m-manylinux2010_x86_64.whl

Hashes for scikit_learn_extra-0.3.0-cp36-cp36m-manylinux2010_x86_64.whl
Algorithm	Hash digest
SHA256	`15681200d239bd414ea616506c7be7c97f33abb88ade944e7a225b42b73bbfa8`
MD5	`86b560278871de850b03d0954ed6587b`
BLAKE2b-256	`2e2e410e409cdee011a8bcfa1384334cec86de097a39067f80444de8727dc93d`

Hashes for scikit_learn_extra-0.3.0-cp36-cp36m-manylinux2010_i686.whl

Hashes for scikit_learn_extra-0.3.0-cp36-cp36m-manylinux2010_i686.whl
Algorithm	Hash digest
SHA256	`9ee6812725b706ae53818845718050c439cc3e02031dc81ed72d6a2679c3f694`
MD5	`242998be197de6bf7af5c568c5adf975`
BLAKE2b-256	`0850ce76004da5d9438ed9fd0f52f264eebadc0e11d770082dacc9d50ce35636`

Hashes for scikit_learn_extra-0.3.0-cp36-cp36m-manylinux1_x86_64.whl

Hashes for scikit_learn_extra-0.3.0-cp36-cp36m-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`8df4782c79cf8c0125523d48e87f00ff8e001004e4ed57743e983f02b10a63cf`
MD5	`0ea5fd405a06902e553553eea0acabf1`
BLAKE2b-256	`0d3ef9e8ec96280d0d1b045abc2b8ffec67662ff60c5d2d93a51d957e6eed239`

Hashes for scikit_learn_extra-0.3.0-cp36-cp36m-manylinux1_i686.whl

Hashes for scikit_learn_extra-0.3.0-cp36-cp36m-manylinux1_i686.whl
Algorithm	Hash digest
SHA256	`cf2cb09d1fa100a9c8ff8eb3561f69af3c73d94191652873521039030519a646`
MD5	`56a63dcf6eeee3e51dbb63547301e6c6`
BLAKE2b-256	`f01cc4593450380d441480bbfa22a590dde43c2973b8fed4fd816ec3ea8e844b`

Hashes for scikit_learn_extra-0.3.0-cp36-cp36m-macosx_10_9_x86_64.whl

Hashes for scikit_learn_extra-0.3.0-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm	Hash digest
SHA256	`837c6805f1948bab0172ff85470fbf568cfb56e8b6a371b892c01f80e5064b67`
MD5	`c2306c2c86ca1429ea94a81bcb071a39`
BLAKE2b-256	`27ead79d06ed5465281d1a3f8a57d11090a52b5bbe55e9dda113c17da8df4425`