Fully Bayesian Classification with a subset of high-dimensional features, such as expression levels of genes. The data are modeled with a hierarchical Bayesian models using heavy-tailed t distributions as priors. When a large number of features are available, one may like to select only a subset of features to use, typically those features strongly correlated with the response in training cases. Such a feature selection procedure is however invalid since the relationship between the response and the features has be exaggerated by feature selection. This package provides a way to avoid this bias and yield better-calibrated predictions for future cases when one uses F-statistic to select features.
Package details |
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| Author | Longhai Li [aut, cre] |
| Maintainer | Longhai Li <longhai.li@usask.ca> |
| License | GPL (>= 2) |
| Version | 1.0-2 |
| URL | https://www.r-project.org https://longhaisk.github.io/ |
| Package repository | View on CRAN |
| Installation |
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