Data quality issues such as missing values and outliers are often
interdependent, which makes preprocessing both time-consuming and leads to
suboptimal performance in knowledge discovery tasks. This package supports
preprocessing decision making by visualizing interdependent data quality issues
through means of feature construction. The user can define his own application
domain specific constructed features that express the quality of a data point
such as number of missing values in the point or use nine default features.
The outcome can be explored with plot methods and the feature constructed data
acquired with get methods.
| Version: |
0.2.0 |
| Depends: |
R (≥ 3.2.2) |
| Imports: |
caret, DMwR, randomForest, ClustOfVar, reshape2, ggplot2, ggdendro, gridExtra, methods, utils, stats |
| Suggests: |
testthat, rmarkdown, knitr, preprocomb |
| Published: |
2016-07-09 |
| Author: |
Markus Vattulainen [aut, cre] |
| Maintainer: |
Markus Vattulainen <markus.vattulainen at gmail.com> |
| BugReports: |
https://github.com/mvattulainen/preproviz/issues |
| License: |
GPL-2 |
| URL: |
https://github.com/mvattulainen/preproviz |
| NeedsCompilation: |
no |
| CRAN checks: |
preproviz results |