The mlscorecheck package provides numerical techniques for testing if a set of reported machine learning performance scores could have resulted from an assumed experimental setup. It enables users to check the consistency of reported scores with the actual experimental setup, helping to address the reproducibility crisis in machine learning and artificial intelligence. Through various use cases and test bundles, the package offers a systematic approach to validating machine learning performance scores across different research areas.
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Testing the Consistency of Reported Machine Learning Performance Scores by mlscorecheck Package
The mlscorecheck package provides practical solutions for testing the consistency between reported machine learning performance scores and experimental setups. By using numerical techniques, the package can help identify unreliable performance scores, contributing to the reproducibility of machine learning and artificial intelligence.
Introduction
In both research and applications, supervised learning approaches are routinely ranked by performance scores. However, due to various factors such as typos, data leakage, and publication bias, reported scores can be unreliable. The mlscorecheck package aims to address this by providing consistency testing capabilities.
Operation of Consistency Tests
The package implements numerical tests to check if the reported scores are consistent with the experimental setup. The tests are conclusive and provide evidence against any inconsistencies found.
Use Cases
Consistency testing has three requirements: the collection of reported performance scores, estimated numerical uncertainty of the scores, and details of the experiment. The package supports testing for binary classification, multiclass classification, and regression problems.
Test Bundles
The mlscorecheck package includes specifications for numerous experimental setups for popular research problems, facilitating the validation of machine learning performance scores. These include retinal vessel segmentation, skin lesion classification, and term-preterm delivery prediction from electrohysterogram signals.
Call for Contribution
Experts from any field are welcome to submit further test bundles to facilitate the validation of machine learning performance scores in various areas of research.
Conclusions
The functionalities provided by the mlscorecheck package enable a more concise, numerical approach to the meta-analysis of machine learning research, contributing to maintaining the integrity of various research fields.
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