The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
A Review of Evaluation Metrics in Machine Learning Algorithms
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Citations
Cites
A Review on Evaluation Metrics for Data Classification Evaluations
M, H., & M.N, S. (2015) · International Journal of Data Mining & Knowledge Management Process · doi:10.5121/ijdkp.2015.5201 · Cited by 2,693
A comparison of machine learning techniques for customer churn prediction
Vafeiadis, T., Diamantaras, K. I., Sarigiannidis, G., & Chatzisavvas, K. C. (2015) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2015.03.003 · Cited by 471
Customer churn prediction in telecom using machine learning in big data platform
Ahmad, A. K., Jafar, A., & Aljoumaa, K. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0191-6 · Cited by 421
Ullah, I., Raza, B., Malik, A. K., Imran, M., Islam, S. U., & Kim, S. W. (2019) · IEEE Access · doi:10.1109/access.2019.2914999 · Cited by 393
Customer churn prediction system: a machine learning approach
Lalwani, P., Mishra, M. K., Chadha, J. S., & Sethi, P. (2022) · Computing · doi:10.1007/s00607-021-00908-y · Cited by 326
Churn Prediction in Telecommunication using Logistic Regression and Logit Boost
Jain, H., Khunteta, A., & Srivastava, S. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.03.187 · Cited by 184
ROC analysis of classifiers in machine learning: A survey
Majnik, M., & Bosnić, Z. (2013) · Intelligent Data Analysis · doi:10.3233/ida-130592 · Cited by 153
A review of supervised machine learning applied to ageing research
Fabris, F., de Magalhães, J. P., & Freitas, A. A. (2017) · Biogerontology · doi:10.1007/s10522-017-9683-y · Cited by 141
Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping
Xu, T., Ma, Y., & Kim, K. (2021) · Applied Sciences · doi:10.3390/app11114742 · Cited by 102
Baldominos, A., Cervantes, A., Sáez, Y., & Isasi, P. (2019) · Sensors · doi:10.3390/s19030521 · Cited by 77
Cited by
Establishing and evaluating trustworthy AI: overview and research challenges
Kowald, D., Scher, S., Pammer-Schindler, V., Müllner, P., Waxnegger, K., Demelius, L., Fessl, A., Toller, M., Mendoza Estrada, I. G., Šimić, I., et al. (2024) · Frontiers in Big Data · doi:10.3389/fdata.2024.1467222 · Cited by 59
Marey, A., Serdysnki, K. C., Killeen, B. D., Unberath, M., & Umair, M. (2024) · BJR|Artificial Intelligence · doi:10.1093/bjrai/ubae008 · Cited by 7
Metadata
- Authors
- Naidu, G., Zuva, T., & Sibanda, E. M.
- Venue
- Lecture Notes in Networks and Systems
- Publisher
- Springer International Publishing
- Published
- 2023
- Pages
- 15-25
- ISSN
- 2367-3370 (ISSN-L)
- 2367-3389
- OpenAlex
- W4383619745
- Cited by
- 494
- References
- 14
- FWCI
- 93.38
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Naidu, G., Zuva, T., & Sibanda, E. M. (2023). A Review of Evaluation Metrics in Machine Learning Algorithms. Lecture Notes in Networks and Systems. https://doi.org/10.1007/978-3-031-35314-7_2
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
