Bedolla, C.N.; Gonzalez, J.M.; Vega, S.J.; Convertino, V.A.; Snider, E.J.
An Explainable Machine-Learning Model for Compensatory Reserve Measurement: Methods for Feature Selection and the Effects of Subject Variability. Bioengineering 2023, 10, 612.
https://doi.org/10.3390/bioengineering10050612
AMA Style
Bedolla CN, Gonzalez JM, Vega SJ, Convertino VA, Snider EJ.
An Explainable Machine-Learning Model for Compensatory Reserve Measurement: Methods for Feature Selection and the Effects of Subject Variability. Bioengineering. 2023; 10(5):612.
https://doi.org/10.3390/bioengineering10050612
Chicago/Turabian Style
Bedolla, Carlos N., Jose M. Gonzalez, Saul J. Vega, VÃctor A. Convertino, and Eric J. Snider.
2023. "An Explainable Machine-Learning Model for Compensatory Reserve Measurement: Methods for Feature Selection and the Effects of Subject Variability" Bioengineering 10, no. 5: 612.
https://doi.org/10.3390/bioengineering10050612
APA Style
Bedolla, C. N., Gonzalez, J. M., Vega, S. J., Convertino, V. A., & Snider, E. J.
(2023). An Explainable Machine-Learning Model for Compensatory Reserve Measurement: Methods for Feature Selection and the Effects of Subject Variability. Bioengineering, 10(5), 612.
https://doi.org/10.3390/bioengineering10050612