credit

A dataset containing the attributes of people who did or did not default on their loan.

datasets

Developed to assist in discovering interesting subgroups in high-dimensional data.
The PRIM implementation is based on the 1998 paper "Bump hunting in high-dimensional data" by Jerome H. Friedman and Nicholas I. Fisher <doi:10.1023/A:1008894516817>.
PRIM involves finding a set of "rules" which combined imply unusually large values of some other target variable.
Specifically one tries to find a set of sub regions in which the target variable is substantially larger than overall mean.
The objective of bump hunting in general is to find regions in the input (attribute/feature) space with relatively high values for the target variable.
The regions are described by simple rules of the type if: condition-1 and ... and condition-n then: estimated target value. Given the data (or a subset of the data),
the goal is to produce a box B within which the target mean is as large as possible.

Jurian Baas

subgroup.discovery

Subgroup Discovery and Bump Hunting

Ad Feelders

credit function

A data frame with 10 rows and 6 variables:<dl class="dl-horizontal">
 <dt>age</dt><dd>age of person</dd>
 <dt>married</dt><dd>is the person married or not, boolean</dd>
 <dt>house</dt><dd>is the person a homeowner or not, boolean</dd>
 <dt>income</dt><dd>income of person, in thousands</dd>
 <dt>gender</dt><dd>factor with levels male, female</dd>
 <dt>class</dt><dd>class variable (0 or 1)</dd>
</dl>

Format

Credit scoring data. — credit

A data frame with 10 rows and 6 variables:<dl class='dl-horizontal'>
 <dt>age</dt><dd>age of person</dd>
 <dt>married</dt><dd>is the person married or not, boolean</dd>
 <dt>house</dt><dd>is the person a homeowner or not, boolean</dd>
 <dt>income</dt><dd>income of person, in thousands</dd>
 <dt>gender</dt><dd>factor with levels male, female</dd>
 <dt>class</dt><dd>class variable (0 or 1)</dd>
</dl>

credit: Credit scoring data.

Description

Usage

Arguments

Format

Details