audit

The audit dataset is an artificially constructed dataset that has some
 of the characteristics of a true financial audit dataset for modelling
 productive and non-productive audits of a person's financial
 statement. A productive audit is one which identifies errors or
 inaccuracies in the information provided by a client. A non-productive
 audit is usually an audit which found all supplied information to be
 in order. The audit dataset is used to illustrate binary classification. The
 target variable is identified as <code>TARGET_Adjusted</code>. The dataset is quite small, consisting of just 2000 entities. Its
 primary purpose is to illustrate modelling in Rattle, so a minimally
 sized dataset is suitable. The dataset itself is derived from publicly available data (which has
 nothing to do with audits). See <a href="/link/acquireAuditData?package=rattle&version=3.0.2" rd-options="" data-mini-rdoc="rattle::acquireAuditData">acquireAuditData</a> for details.

datasets

Rattle (the R Analytic Tool To Learn Easily) provides a 
  Gnome (RGtk2) based interface to R functionality for data mining. 
  The aim is to provide a simple and intuitive interface 
  that allows a user to quickly load data from a CSV file 
  (or via ODBC), transform and explore the data, 
  build and evaluate models, and export models as PMML (predictive
  modelling markup language) or as scores. All of this with knowing little 
  about R. All R commands are logged and commented through the log tab. Thus they
  are available to the user as a script file or as an aide for the user to 
  learn R or to copy-and-paste directly into R itself. 
  Rattle also exports a number of utility 
  functions and the graphical user interface, invoked as rattle(),  does 
  not need to be run to deploy these.  

audit: Sample dataset for illustration Rattle functionality.

Description

Arguments