Can I read Machine Learning Evaluation Metric Discrepancies across Programming Languages and Their Components: Need for Standardization on EtoBox?

Machine Learning Evaluation Metric Discrepancies across Programming Languages and Their Components: Need for Standardization by Salmanpour, Mohammad R.; Alizadeh, Morteza; Mousavi, Ghazal; Sadeghi, Saba; Amiri, Sajad; Oveisi, Mehrdad; Rahmim, Arman; Hacihaliloglu, Ilker is a scholarly article available to read on EtoBox.

What is Machine Learning Evaluation Metric Discrepancies across Programming Languages and Their Components: Need for Standardization about?

This study evaluates metrics for tasks such as classification, regression, clustering, correlation analysis, statistical tests, segmentation, and image-to-image (I2I) translation. Metrics were compared across Python libraries, R packages, and Matlab functions to assess their consistency and highlight discrepancies. The findings underscore the need for a unified roadmap to standardize metrics, ensuring reliable and reproducible ML evaluations across platforms. This study examined a wide range of evaluation metrics across various tasks and found only some to be consistent across platforms, such as (i) Accuracy, Balanced Accuracy, Cohens Kappa, F-beta Score, MCC, Geometric Mean, AUC, and Log Loss in binary classification; (ii) Accuracy, Cohens Kappa, and F-beta Score in multi-class classification; (iii) MAE, MSE, RMSE, MAPE, Explained Variance, Median AE, MSLE, and Huber in regression; (iv) Davies-Bouldin Index and Calinski-Harabasz Index in clustering; (v) Pearson, Spearman, Kendall's Tau, Mutual Information, Distance Correlation, Percbend, Shepherd, and Partial Correlation in correlation analysis; (vi) Paired t-test, Chi-Square Test, ANOVA, Kruskal-Wallis Test, Shapiro-Wilk Test, We

Author

Salmanpour, Mohammad R.; Alizadeh, Morteza; Mousavi, Ghazal; Sadeghi, Saba; Amiri, Sajad; Oveisi, Mehrdad; Rahmim, Arman; Hacihaliloglu, Ilker

Published

2024

Language

EN