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HotSpot (3.7)

KNIME WEKA nodes (3.7) version 4.4.0.v202104271009 by KNIME AG, Zurich, Switzerland

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.


HotSpot Options

c: The target index. (default = last)

V: The target value (nominal target only, default = first)

L: Minimize rather than maximize.

S: Minimum value count (nominal target)/segment size (numeric target). Values between 0 and 1 are interpreted as a percentage of the total population; values > 1 are interpreted as an absolute number of instances (default = 0.3)

M: Maximum branching factor (default = 2)

length: Maximum rule length (default = -1, i.e. no maximum)

I: Minimum improvement in target value in order to add a new branch/test (default = 0.01 (1%))

Z: Treat zero (first value) as missing for nominal attributes

R: Output a set of rules instead of a tree structure

D: Output debugging info (duplicate rule lookup hash table stats)

Preliminary Attribute Check

The Preliminary Attribute Check tests the underlying classifier against the DataTable specification at the inport of the node. Columns that are compatible with the classifier are marked with a green 'ok'. Columns which are potentially not compatible are assigned a red error message.

Important: If a column is marked as 'incompatible', it does not necessarily mean that the classifier cannot be executed! Sometimes, the error message 'Cannot handle String class' simply means that no nominal values are available (yet). This may change during execution of the predecessor nodes.

Capabilities: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Missing values, No class] Dependencies: [] min # Instance: 1

Command line options

It shows the command line options according to the current classifier configuration and mainly serves to support the node's configuration via flow variables.

Input Ports

Training data


Weka Node View
Each Weka node provides a summary view that provides information about the classification. If the test data contains a class column, an evaluation is generated.


To use this node in KNIME, install KNIME Weka Data Mining Integration (3.7) from the following update site:


A zipped version of the software site can be downloaded here.

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