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.
|Author||Markus Vattulainen [aut, cre]|
|Maintainer||Markus Vattulainen <email@example.com>|
|Package repository||View on CRAN|
Install the latest version of this package by entering the following in R:
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.