MLcps: machine learning cumulative performance score for classification problems.

DOI
Publisher DOI
PubMed ID
38091508
Abstract
BACKGROUND

Assessing the performance of machine learning (ML) models requires careful consideration of the evaluation metrics used. It is often necessary to utilize multiple metrics to gain a comprehensive understanding of a trained model's performance, as each metric focuses on a specific aspect. However, comparing the scores of these individual metrics for each model to determine the best-performing model can be time-consuming and susceptible to subjective user preferences, potentially introducing bias.

RESULTS

We propose the Machine Learning Cumulative Performance Score (MLcps), a novel evaluation metric for classification problems. MLcps integrates several precomputed evaluation metrics into a unified score, enabling a comprehensive assessment of the trained model's strengths and weaknesses. We tested MLcps on 4 publicly available datasets, and the results demonstrate that MLcps provides a holistic evaluation of the model's robustness, ensuring a thorough understanding of its overall performance.

CONCLUSIONS

By utilizing MLcps, researchers and practitioners no longer need to individually examine and compare multiple metrics to identify the best-performing models. Instead, they can rely on a single MLcps value to assess the overall performance of their ML models. This streamlined evaluation process saves valuable time and effort, enhancing the efficiency of model evaluation. MLcps is available as a Python package at https://pypi.org/project/MLcps/.
Date Issued
2022-12-28
Publication Type
Article
Subject(s)
Subjects
Python package classification problems machine learning model evaluation unified evaluation score
Language(s)
en
Author(s)
Abedi, Masoud
Shekarchizadeh, Navid
Katoch, Mitali
Bigger-Allen, Alex
Adam, Rosalyn M
Additional Credits
Journal
GigaScience
Publisher
Oxford University Press
ISSN
2047-217X
Access(Rights)
open.access
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