MLcps: machine learning cumulative performance score for classification problems.

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 stre

SUBMITTER: Akshay A 

PROVIDER: S-EPMC10716825 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature