A comparison of random forest regression and multiple linear regression for prediction in neuroscience

@article{Smith2013ACO,
  title={A comparison of random forest regression and multiple linear regression for prediction in neuroscience},
  author={Paul F. Smith and Siva Ganesh and Ping Liu},
  journal={Journal of Neuroscience Methods},
  year={2013},
  volume={220},
  pages={85-91}
}
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77 Citations
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77 Citations

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On the Application of Multivariate Statistical and Data Mining Analyses to Data in Neuroscience.
  • Paul F. Smith
  • Biology
    Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience
  • 2018
TLDR
Methods such as linear discriminant analysis, support vector machines, principal component and factor analysis, cluster analysis, multiple linear regression, and random forest regression and classification are reviewed to provide a succinct guide to methods used in circumscribed areas of neuroscience research, but which could be used more widely.
Applications of Multivariate Statistical and Data Mining Analyses to the Search for Biomarkers of Sensorineural Hearing Loss, Tinnitus, and Vestibular Dysfunction
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Age-Related Neurochemical Changes in the Vestibular Nuclei
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Quantifying the Reproducibility of Graph Neural Networks using Multigraph Brain Data
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Multitrait machine‐ and deep‐learning models for genomic selection using spectral information in a wheat breeding program
TLDR
Overall, this study concluded that machine‐ and deep‐learning‐based MT‐GS models increased prediction accuracy and should be employed in large‐scale breeding programs.
Machine Learning Tools to Assess the Impact of COVID-19 Civil Measures in Atmospheric Pollution
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This work research the relation between the COVID-19 measures and the Air Quality Index (AQI), using four pollutant gases (CO, O3, NO2, SO2), using a variety of machine learning tools to estimate the accuracy of each method in the prediction of the concentration for each gas one week later.
Using Random Forest Regression to Determine Influential Force-Time Metrics for Countermovement Jump Height: A Technical Report
Abstract Merrigan, JJ, Stone, JD, Wagle, JP, Hornsby, WG, Ramadan, J, Joseph, M, and Hagen, JA. Using random forest regression to determine influential force-time metrics for countermovement jump…
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References

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