audit


    This is an artificial dataset consisting of fictional clients who have 
    been audited, perhaps for tax refund compliance. For each case an outcome 
    is recorded (whether the taxpayer's claims had to be adjusted or not) and 
    any amount of adjustment that resulted is also recorded.


datasets

The Predictive Model Markup Language (PMML) is an XML-based
language which provides a way for applications to define statistical and
data mining models and to share models between PMML compliant applications.
More information about PMML and the Data Mining Group can be found at http://
www.dmg.org. The generated PMML can be imported into any PMML consuming
application, such as the Zementis ADAPA and UPPI scoring engines which allow for
predictive models built in R to be deployed and executed on site, in the cloud
(Amazon, IBM, and FICO), in-database (IBM Netezza, Pivotal, Sybase IQ, Teradata
and Teradata Aster) or Hadoop (Datameer and Hive).

Tridivesh Jena

pmml

Generate PMML for Various Models

audit function

A data frame containing:
    <table><tr><td>
        Age        </td>
</tr><tr><td> Numeric </td>
</tr><tr><td>
        Employment </td>
</tr><tr><td> Categorical string with 7 levels </td>
</tr><tr><td> 
        Education  </td>
</tr><tr><td> Categorical string with 16 levels </td>
</tr><tr><td>
        Marital    </td>
</tr><tr><td> Categorical string with 6 levels </td>
</tr><tr><td>
        Occupation </td>
</tr><tr><td> Categorical string with 14 levels </td>
</tr><tr><td>
        Income     </td>
</tr><tr><td> Numeric </td>
</tr><tr><td>
        Sex        </td>
</tr><tr><td> Categorical string with 2 levels </td>
</tr><tr><td>
        Deductions </td>
</tr><tr><td> Numeric </td>
</tr><tr><td>
	Hours      </td>
</tr><tr><td> Numeric </td>
</tr><tr><td>
	Accounts   </td>
</tr><tr><td> Categorical string with 32 levels </td>
</tr><tr><td>
	Adjustment </td>
</tr><tr><td> Numeric </td>
</tr><tr><td>
	Adjusted   </td>
</tr><tr><td> Numeric value 0 or 1 </td>
</tr></table>

audit: Artificially constructed dataset

Description

Arguments

Format

References