Cargando…
Evaluation methodology for deep learning imputation models
There is growing interest in imputing missing data in tabular datasets using deep learning. Existing deep learning–based imputation models have been commonly evaluated using root mean square error (RMSE) as the predictive accuracy metric. In this article, we investigate the limitations of assessing...
Autores principales: | Boursalie, Omar, Samavi, Reza, Doyle, Thomas E. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9791304/ https://www.ncbi.nlm.nih.gov/pubmed/36562377 http://dx.doi.org/10.1177/15353702221121602 |
Ejemplares similares
-
Re-Evaluation of Genotyping Methodologies in Cattle: The Proficiency of Imputation
por: Gershoni, Moran, et al.
Publicado: (2023) -
Deep Learning Methods for Omics Data Imputation
por: Huang, Lei, et al.
Publicado: (2023) -
SemImput: Bridging Semantic Imputation with Deep Learning for Complex Human Activity Recognition
por: Razzaq, Muhammad Asif, et al.
Publicado: (2020) -
Imputation by feature importance (IBFI): A methodology to envelop machine learning method for imputing missing patterns in time series data
por: Mir, Adil Aslam, et al.
Publicado: (2022) -
An autoencoder-based deep learning method for genotype imputation
por: Song, Meng, et al.
Publicado: (2022)