HotSpot

Package

weka.associations

Synopsis

HotSpot learns a set of rules (displayed in a tree-like structure) that maximize/minimize a target variable/value of interest. With a nominal target, one might want to look for segments of the data where there is a high probability of a minority value occuring (given the constraint of a minimum support). For a numeric target, one might be interested in finding segments where this is higher on average than in the whole data set. For example, in a health insurance scenario, find which health insurance groups are at the highest risk (have the highest claim ratio), or, which groups have the highest average insurance payout.

Available in Weka 3.7.1. Available via the package management system for Weka >= 3.7.2 (hotSpot).

Options

The table below describes the options available for HotSpot.

Option	Description
debug	Output debugging info (duplicate rule lookup hash table stats).
maxBranchingFactor	Maximum branching factor. The maximum number of children to consider extending each node with.
minImprovement	Minimum improvement in target value in order to consider adding a new branch/test
minimizeTarget	Minimize rather than maximize the target.
support	The minimum support. Values between 0 and 1 are interpreted as a percentage of the total population; values > 1 are interpreted as an absolute number of instances
target	The target attribute of interest.
targetIndex	The value of the target (nominal attributes only) of interest.

Capabilities

The table below describes the capabilites of HotSpot.

Capability	Supported
Class	Numeric class, Nominal class, Binary class
Attributes	Empty nominal attributes, Unary attributes, Nominal attributes, Missing values, Binary attributes, Numeric attributes
Min # of instances	1