DataClass-class


DataClass object can be initialized only for a data frame that has a) one class label columns of class 'factor' and
b) other columns are of type 'numeric'


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.

Markus Vattulainen

preproviz

Tools for Visualization of Interdependent Data Quality Issues

DataClass-class function

 <dl>
<dt><code>name</code></dt><dd>(character) name of the setup object</dd> <dt><code>basedata</code></dt><dd>(data frame) original data to be visualized</dd> <dt><code>imputedbase</code></dt><dd>(data frame) missing value in original data imputed with Knn imputation</dd> <dt><code>numericdata</code></dt><dd>(data frame) numeric columns of original data</dd> <dt><code>imputednumeric</code></dt><dd>(data frame) imputed numeric columns</dd> </dl>

DataClass-class: An S4 class representing data objects

Description

Arguments

Slots