Ridor (3.7)

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 3.7)

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.


Ridor 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 1)

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)

Select target 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

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.

Additional Options

Select optional vector column
If the input table contains vector columns (e.g. double vector), the one to use can be selected here. This vector column will be used as attributes only and all other columns, except the target column, will be ignored.
Keep training instances
If checked, all training instances will be kept and stored with the classifier model. It is useful to calculate additional evaluation measures (see Weka Predictor) that make use of class prior probabilities. If no evaluation is performed or those measures are not required, it is advisable to NOT keep the training instances.

Input Ports

Training data

Output Ports

Trained model

Popular Predecessors

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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.


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