audit

<p>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.</p>

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 Software AG Zementis scoring engine, which allows 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).

Dmitriy Bolotov

pmml

Generate PMML for Various Models

audit function

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

audit: Artificially constructed dataset

Description

Arguments

Format

References

Examples

Age	Numeric
Employment	Categorical string with 7 levels
Education	Categorical string with 16 levels
Marital	Categorical string with 6 levels
Occupation	Categorical string with 14 levels
Income	Numeric
Sex	Categorical string with 2 levels
Deductions	Numeric
Hours	Numeric
Accounts	Categorical string with 32 levels
Adjustment	Numeric
Adjusted	Numeric value 0 or 1