NodeHotSpot (3.7)

Learner

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.

(based on WEKA 3.7)

For further options, click the 'More' - button in the dialog.

All weka dialogs have a panel where you can specify classifier-specific parameters.

Input Ports

  1. Port Type: Data
    Training data