This software is used to predict the binary response based on high dimensional features, for example gene expression data. The data are modelled with Bayesian naive Bayes models. 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 will appear stronger. This package provides a way to avoid this bias and yields well-calibrated prediction for the test cases.
| Version: | 1.1-4 |
| Depends: | R (≥ 2.5.1) |
| Author: | Longhai Li |
| Maintainer: | Longhai Li <longhai at math.usask.ca> |
| License: | GPL (≥2) |
| URL: | \url{http://www.r-project.org}, \url{http://math.usask.ca/~longhai} |
| In views: | MachineLearning, Multivariate |
| CRAN checks: | predbayescor results |
Downloads:
| Package source: | predbayescor_1.1-4.tar.gz |
| MacOS X binary: | predbayescor_1.1-4.tgz |
| Windows binary: | predbayescor_1.1-4.zip |
| Reference manual: | predbayescor.pdf |
| Old sources: | predbayescor archive |