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Title: GitHub - FalseNegativeLab/mlscorecheck: Testing the consistency of binary classification performance scores reported in papers · GitHub

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Description: Testing the consistency of binary classification performance scores reported in papers - FalseNegativeLab/mlscorecheck

Open Graph Description: Testing the consistency of binary classification performance scores reported in papers - FalseNegativeLab/mlscorecheck

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CODE_OF_CONDUCT.md	https://github.com/FalseNegativeLab/mlscorecheck/blob/main/CODE_OF_CONDUCT.md
CODE_OF_CONDUCT.md	https://github.com/FalseNegativeLab/mlscorecheck/blob/main/CODE_OF_CONDUCT.md
LICENSE	https://github.com/FalseNegativeLab/mlscorecheck/blob/main/LICENSE
LICENSE	https://github.com/FalseNegativeLab/mlscorecheck/blob/main/LICENSE
README.rst	https://github.com/FalseNegativeLab/mlscorecheck/blob/main/README.rst
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requirements.txt	https://github.com/FalseNegativeLab/mlscorecheck/blob/main/requirements.txt
requirements.txt	https://github.com/FalseNegativeLab/mlscorecheck/blob/main/requirements.txt
setup.py	https://github.com/FalseNegativeLab/mlscorecheck/blob/main/setup.py
setup.py	https://github.com/FalseNegativeLab/mlscorecheck/blob/main/setup.py
README	https://github.com/FalseNegativeLab/mlscorecheck
Code of conduct	https://github.com/FalseNegativeLab/mlscorecheck
MIT license	https://github.com/FalseNegativeLab/mlscorecheck
	https://github.com/gykovacs/mlscorecheck/workflows/Python%20package/badge.svg?branch=main
	https://codecov.io/gh/FalseNegativeLab/mlscorecheck
	https://img.shields.io/badge/pylint-10.0-brightgreen
	https://mlscorecheck.readthedocs.io/en/latest/?badge=latest
	https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10%20%7C%203.11-brightgreen
	https://badge.fury.io/py/mlscorecheck
	https://img.shields.io/badge/license-MIT-brightgreen
	https://app.gitter.im/#/room/!AmkvUevcfkobbwcNWS:gitter.im
mlscorecheck: testing the consistency of machine learning performance scores	https://github.com/FalseNegativeLab/mlscorecheck#id14
	https://github.com/FalseNegativeLab/mlscorecheck#mlscorecheck-testing-the-consistency-of-machine-learning-performance-scores
mlscorecheck: testing the consistency of machine learning performance scores	https://github.com/FalseNegativeLab/mlscorecheck#mlscorecheck-testing-the-consistency-of-machine-learning-performance-scores
Getting started	https://github.com/FalseNegativeLab/mlscorecheck#getting-started
The purpose	https://github.com/FalseNegativeLab/mlscorecheck#the-purpose
Latest news	https://github.com/FalseNegativeLab/mlscorecheck#latest-news
Citation	https://github.com/FalseNegativeLab/mlscorecheck#citation
Installation	https://github.com/FalseNegativeLab/mlscorecheck#installation
Introduction	https://github.com/FalseNegativeLab/mlscorecheck#introduction
Preliminaries	https://github.com/FalseNegativeLab/mlscorecheck#preliminaries
Requirements	https://github.com/FalseNegativeLab/mlscorecheck#requirements
Specification of the experimental setup	https://github.com/FalseNegativeLab/mlscorecheck#specification-of-the-experimental-setup
A note on the Score of Means and Mean of Scores aggregations	https://github.com/FalseNegativeLab/mlscorecheck#a-note-on-the-score-of-means-and-mean-of-scores-aggregations
The types of tests	https://github.com/FalseNegativeLab/mlscorecheck#the-types-of-tests
Binary classification	https://github.com/FalseNegativeLab/mlscorecheck#binary-classification
1 testset with no k-fold	https://github.com/FalseNegativeLab/mlscorecheck#testset-with-no-k-fold
1 dataset with k-fold, mean of scores (MoS)/score of means (SoM) aggregation	https://github.com/FalseNegativeLab/mlscorecheck#dataset-with-k-fold-mean-of-scores-mos-score-of-means-som-aggregation
n testsets without k-fold, SoM/MoS aggregation	https://github.com/FalseNegativeLab/mlscorecheck#n-testsets-without-k-fold-som-mos-aggregation
n datasets with k-fold, MoS over datasets and MoS over folds	https://github.com/FalseNegativeLab/mlscorecheck#n-datasets-with-k-fold-mos-over-datasets-and-mos-over-folds
Not knowing the k-folding scheme	https://github.com/FalseNegativeLab/mlscorecheck#not-knowing-the-k-folding-scheme
Not knowing the mode of aggregation	https://github.com/FalseNegativeLab/mlscorecheck#not-knowing-the-mode-of-aggregation
Multiclass classification	https://github.com/FalseNegativeLab/mlscorecheck#multiclass-classification
A note on micro and macro-averaging	https://github.com/FalseNegativeLab/mlscorecheck#a-note-on-micro-and-macro-averaging
1 testset, no k-fold, micro/macro-averaging	https://github.com/FalseNegativeLab/mlscorecheck#testset-no-k-fold-micro-macro-averaging
1 dataset, known k-folds, SoM/MoS aggregation, micro/macro-averaging	https://github.com/FalseNegativeLab/mlscorecheck#dataset-known-k-folds-som-mos-aggregation-micro-macro-averaging
Regression	https://github.com/FalseNegativeLab/mlscorecheck#regression
1 testset, no k-fold	https://github.com/FalseNegativeLab/mlscorecheck#testset-no-k-fold
Test bundles	https://github.com/FalseNegativeLab/mlscorecheck#test-bundles
Contribution	https://github.com/FalseNegativeLab/mlscorecheck#contribution
References	https://github.com/FalseNegativeLab/mlscorecheck#references
Getting started	https://github.com/FalseNegativeLab/mlscorecheck#id15
	https://github.com/FalseNegativeLab/mlscorecheck#getting-started
The purpose	https://github.com/FalseNegativeLab/mlscorecheck#id16
	https://github.com/FalseNegativeLab/mlscorecheck#the-purpose
Latest news	https://github.com/FalseNegativeLab/mlscorecheck#id17
	https://github.com/FalseNegativeLab/mlscorecheck#latest-news
https://arxiv.org/abs/2310.12527	https://arxiv.org/abs/2310.12527
https://mlscorecheck.readthedocs.io/en/latest/	https://mlscorecheck.readthedocs.io/en/latest/
https://medium.com/p/e1baaba57610	https://medium.com/p/e1baaba57610
Citation	https://github.com/FalseNegativeLab/mlscorecheck#id18
	https://github.com/FalseNegativeLab/mlscorecheck#citation
Installation	https://github.com/FalseNegativeLab/mlscorecheck#id19
	https://github.com/FalseNegativeLab/mlscorecheck#installation
Introduction	https://github.com/FalseNegativeLab/mlscorecheck#id20
	https://github.com/FalseNegativeLab/mlscorecheck#introduction
https://en.wikipedia.org/wiki/Evaluation_of_binary_classifiers	https://en.wikipedia.org/wiki/Evaluation_of_binary_classifiers
https://en.wikipedia.org/wiki/Replication_crisis	https://en.wikipedia.org/wiki/Replication_crisis
[RV]	https://github.com/FalseNegativeLab/mlscorecheck#rv
[EHG]	https://github.com/FalseNegativeLab/mlscorecheck#ehg
[RV]	https://github.com/FalseNegativeLab/mlscorecheck#rv
https://mlscorecheck.readthedocs.io/en/latest/	https://mlscorecheck.readthedocs.io/en/latest/
https://arxiv.org/abs/2310.12527	https://arxiv.org/abs/2310.12527
Preliminaries	https://github.com/FalseNegativeLab/mlscorecheck#id21
	https://github.com/FalseNegativeLab/mlscorecheck#preliminaries
Requirements	https://github.com/FalseNegativeLab/mlscorecheck#id22
	https://github.com/FalseNegativeLab/mlscorecheck#requirements
Specification of the experimental setup	https://github.com/FalseNegativeLab/mlscorecheck#id23
	https://github.com/FalseNegativeLab/mlscorecheck#specification-of-the-experimental-setup
	https://github.com/FalseNegativeLab/mlscorecheck#specification-of-one-testset-or-dataset
	https://github.com/FalseNegativeLab/mlscorecheck#specification-of-a-folding
	https://github.com/FalseNegativeLab/mlscorecheck#specification-of-an-evaluation
A note on the Score of Means and Mean of Scores aggregations	https://github.com/FalseNegativeLab/mlscorecheck#id24
	https://github.com/FalseNegativeLab/mlscorecheck#a-note-on-the-score-of-means-and-mean-of-scores-aggregations
The types of tests	https://github.com/FalseNegativeLab/mlscorecheck#id25
	https://github.com/FalseNegativeLab/mlscorecheck#the-types-of-tests
Binary classification	https://github.com/FalseNegativeLab/mlscorecheck#id26
	https://github.com/FalseNegativeLab/mlscorecheck#binary-classification
https://mlscorecheck.readthedocs.io/en/latest/	https://mlscorecheck.readthedocs.io/en/latest/
1 testset with no k-fold	https://github.com/FalseNegativeLab/mlscorecheck#id27
	https://github.com/FalseNegativeLab/mlscorecheck#1-testset-with-no-k-fold
1 dataset with k-fold, mean of scores (MoS)/score of means (SoM) aggregation	https://github.com/FalseNegativeLab/mlscorecheck#id28
	https://github.com/FalseNegativeLab/mlscorecheck#1-dataset-with-k-fold-mean-of-scores-mosscore-of-means-som-aggregation
https://mlscorecheck.readthedocs.io/en/latest/	https://mlscorecheck.readthedocs.io/en/latest/
n testsets without k-fold, SoM/MoS aggregation	https://github.com/FalseNegativeLab/mlscorecheck#id29
	https://github.com/FalseNegativeLab/mlscorecheck#n-testsets-without-k-fold-sommos-aggregation
https://mlscorecheck.readthedocs.io/en/latest/	https://mlscorecheck.readthedocs.io/en/latest/
n datasets with k-fold, MoS over datasets and MoS over folds	https://github.com/FalseNegativeLab/mlscorecheck#id30
	https://github.com/FalseNegativeLab/mlscorecheck#n-datasets-with-k-fold-mos-over-datasets-and-mos-over-folds
https://mlscorecheck.readthedocs.io/en/latest/	https://mlscorecheck.readthedocs.io/en/latest/
Not knowing the k-folding scheme	https://github.com/FalseNegativeLab/mlscorecheck#id31
	https://github.com/FalseNegativeLab/mlscorecheck#not-knowing-the-k-folding-scheme
Not knowing the mode of aggregation	https://github.com/FalseNegativeLab/mlscorecheck#id32
	https://github.com/FalseNegativeLab/mlscorecheck#not-knowing-the-mode-of-aggregation
Multiclass classification	https://github.com/FalseNegativeLab/mlscorecheck#id33
	https://github.com/FalseNegativeLab/mlscorecheck#multiclass-classification
A note on micro and macro-averaging	https://github.com/FalseNegativeLab/mlscorecheck#id34
	https://github.com/FalseNegativeLab/mlscorecheck#a-note-on-micro-and-macro-averaging
1 testset, no k-fold, micro/macro-averaging	https://github.com/FalseNegativeLab/mlscorecheck#id35
	https://github.com/FalseNegativeLab/mlscorecheck#1-testset-no-k-fold-micromacro-averaging
https://mlscorecheck.readthedocs.io/en/latest/	https://mlscorecheck.readthedocs.io/en/latest/
1 dataset, known k-folds, SoM/MoS aggregation, micro/macro-averaging	https://github.com/FalseNegativeLab/mlscorecheck#id36
	https://github.com/FalseNegativeLab/mlscorecheck#1-dataset-known-k-folds-sommos-aggregation-micromacro-averaging
https://mlscorecheck.readthedocs.io/en/latest/	https://mlscorecheck.readthedocs.io/en/latest/
Regression	https://github.com/FalseNegativeLab/mlscorecheck#id37
	https://github.com/FalseNegativeLab/mlscorecheck#regression
1 testset, no k-fold	https://github.com/FalseNegativeLab/mlscorecheck#id38
	https://github.com/FalseNegativeLab/mlscorecheck#1-testset-no-k-fold
Test bundles	https://github.com/FalseNegativeLab/mlscorecheck#id39
	https://github.com/FalseNegativeLab/mlscorecheck#test-bundles
https://mlscorecheck.readthedocs.io/en/latest/	https://mlscorecheck.readthedocs.io/en/latest/
[DRIVE]	https://github.com/FalseNegativeLab/mlscorecheck#drive
[STARE]	https://github.com/FalseNegativeLab/mlscorecheck#stare
[HRF]	https://github.com/FalseNegativeLab/mlscorecheck#hrf
[CHASE_DB1]	https://github.com/FalseNegativeLab/mlscorecheck#chase-db1
[DIARETDB0]	https://github.com/FalseNegativeLab/mlscorecheck#diaretdb0
[DIARETDB1]	https://github.com/FalseNegativeLab/mlscorecheck#diaretdb1
[DRISHTI_GS]	https://github.com/FalseNegativeLab/mlscorecheck#drishti-gs
[ISIC2016]	https://github.com/FalseNegativeLab/mlscorecheck#isic2016
[ISIC2017]	https://github.com/FalseNegativeLab/mlscorecheck#isic2017
[TPEHG]	https://github.com/FalseNegativeLab/mlscorecheck#tpehg
Contribution	https://github.com/FalseNegativeLab/mlscorecheck#id40
	https://github.com/FalseNegativeLab/mlscorecheck#contribution
References	https://github.com/FalseNegativeLab/mlscorecheck#id41
	https://github.com/FalseNegativeLab/mlscorecheck#references
1	https://github.com/FalseNegativeLab/mlscorecheck#id1
2	https://github.com/FalseNegativeLab/mlscorecheck#id3
[EHG]	https://github.com/FalseNegativeLab/mlscorecheck#id2
[DRIVE]	https://github.com/FalseNegativeLab/mlscorecheck#id4
[STARE]	https://github.com/FalseNegativeLab/mlscorecheck#id5
[HRF]	https://github.com/FalseNegativeLab/mlscorecheck#id6
[CHASE_DB1]	https://github.com/FalseNegativeLab/mlscorecheck#id7
[DIARETDB0]	https://github.com/FalseNegativeLab/mlscorecheck#id8
[DIARETDB1]	https://github.com/FalseNegativeLab/mlscorecheck#id9
[DRISHTI_GS]	https://github.com/FalseNegativeLab/mlscorecheck#id10
[ISIC2016]	https://github.com/FalseNegativeLab/mlscorecheck#id11
[ISIC2017]	https://github.com/FalseNegativeLab/mlscorecheck#id12
[TPEHG]	https://github.com/FalseNegativeLab/mlscorecheck#id13
performance	https://github.com/topics/performance
consistency	https://github.com/topics/consistency
specificity	https://github.com/topics/specificity
classification	https://github.com/topics/classification
accuracy	https://github.com/topics/accuracy
recall	https://github.com/topics/recall
retina	https://github.com/topics/retina
sensitivity	https://github.com/topics/sensitivity
f1	https://github.com/topics/f1
precision	https://github.com/topics/precision
scores	https://github.com/topics/scores
skin-lesion	https://github.com/topics/skin-lesion
ehg	https://github.com/topics/ehg
Readme	https://github.com/FalseNegativeLab/mlscorecheck#readme-ov-file
MIT license	https://github.com/FalseNegativeLab/mlscorecheck#MIT-1-ov-file
Code of conduct	https://github.com/FalseNegativeLab/mlscorecheck#coc-ov-file
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Activity	https://github.com/FalseNegativeLab/mlscorecheck/activity
Custom properties	https://github.com/FalseNegativeLab/mlscorecheck/custom-properties
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Report repository	https://github.com/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2FFalseNegativeLab%2Fmlscorecheck&report=FalseNegativeLab+%28user%29
Releases 6	https://github.com/FalseNegativeLab/mlscorecheck/releases
1.0.2 release Latest Nov 16, 2023	https://github.com/FalseNegativeLab/mlscorecheck/releases/tag/v1.0.2
+ 5 releases	https://github.com/FalseNegativeLab/mlscorecheck/releases
Packages 0	https://github.com/orgs/FalseNegativeLab/packages?repo_name=mlscorecheck
Please reload this page	https://github.com/FalseNegativeLab/mlscorecheck
Please reload this page	https://github.com/FalseNegativeLab/mlscorecheck
Contributors	https://github.com/FalseNegativeLab/mlscorecheck/graphs/contributors
Please reload this page	https://github.com/FalseNegativeLab/mlscorecheck
Jupyter Notebook 97.3%	https://github.com/FalseNegativeLab/mlscorecheck/search?l=jupyter-notebook
Python 2.7%	https://github.com/FalseNegativeLab/mlscorecheck/search?l=python
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