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This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels.

A rule consists of antecedents "AND"ed together and the consequent (class value) for the classification/regression. In this case, the consequent is the distribution of the available classes (or mean for a numeric value) in the dataset. If the test instance is not covered by this rule, then it's predicted using the default class distributions/value of the data not covered by the rule in the training data.This learner selects an antecedent by computing the Information Gain of each antecendent and prunes the generated rule using Reduced Error Prunning (REP) or simple pre-pruning based on the number of antecedents.

For classification, the Information of one antecedent is the weighted average of the entropies of both the data covered and not covered by the rule.
For regression, the Information is the weighted average of the mean-squared errors of both the data covered and not covered by the rule.

In pruning, weighted average of the accuracy rates on the pruning data is used for classification while the weighted average of the mean-squared errors on the pruning data is used for regression.

Available in Weka 3.6.x - 3.7.1. Available via the package management system for Weka >= 3.7.2 (conjunctiveRule).


The table below describes the options available for ConjunctiveRule.




If set to true, classifier may output additional info to the console.


Set whether to consider exclusive expressions for nominal attribute splits.


Determines the amount of data used for pruning. One fold is used for pruning, the rest for growing the rules.


The minimum total weight of the instances in a rule.


Set the number of antecedents allowed in the rule if pre-pruning is used. If this value is other than -1, then pre-pruning will be used, otherwise the rule uses reduced-error pruning.


The seed used for randomizing the data.


The table below describes the capabilites of ConjunctiveRule.




Nominal class, Binary class, Date class, Missing class values, Numeric class


Binary attributes, Date attributes, Numeric attributes, Unary attributes, Nominal attributes, Empty nominal attributes, Missing values

Min # of instances


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