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Ridor (3.6)

KNIME WEKA nodes version 2.10.2.v202003301209 by KNIME AG, Zurich, Switzerland

An implementation of a RIpple-DOwn rule learner*. It generates a default rule first and then the exceptions for the default rule with the least (weighted) error rate. Then it generates the "best" exceptions for each exception and iterates until pure. Thus it performs a tree-like expansion of exceptions.The exceptions are a set of rules that predict classes other than the default. IREP is used to generate the exceptions. For more information about Ripple-Down Rules, see:

(based on WEKA 3.6)

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

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

(*) RULE LEARNER is a registered trademark of Minitab, LLC and is used with Minitab's permission.

Options

Class column
Choose the column that contains the target variable.
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, Date attributes, Missing values, Nominal class, Binary class, Missing class values] Dependencies: [] min # Instance: 1

Classifier Options

F: Set number of folds for IREP One fold is used as pruning set. (default 3)

S: Set number of shuffles to randomize the data in order to get better rule. (default 10)

A: Set flag of whether use the error rate of all the data to select the default class in each step. If not set, the learner will only use the error rate in the pruning data

M: Set flag of whether use the majority class as the default class in each step instead of choosing default class based on the error rate (if the flag is not set)

N: Set the minimal weights of instances within a split. (default 2.0)

Input Ports

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Training data

Output Ports

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Trained classifier

Views

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.

Installation

To use this node in KNIME, install KNIME Weka Data Mining Integration (3.6) from the following update site:

KNIME 4.2

A zipped version of the software site can be downloaded here. Read our FAQs to get instructions about how to install nodes from a zipped update site.

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Developers

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