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 machine learning, statistical and data mining models and to share models between PMML compliant applications. More information about the PMML industry standard and the Data Mining Group can be found at <http://dmg.org/>. The generated PMML can be imported into any PMML consuming application, such as Zementis Predictive Analytics products. The package isofor (used for anomaly detection) can be installed with devtools::install_github("gravesee/isofor").

Dmitriy Bolotov

pmml

Generate PMML for Various Models

Tridivesh Jena

Graham Williams

Wen-Ching Lin

Michael Hahsler

Hemant Ishwaran

Udaya B. Kogalur

Rajarshi Guha

Software AG 

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>

Format

Audit: artificially constructed dataset — audit

Audit: artificially constructed dataset

audit: 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