Download JAR files tagged by rule with all dependencies

This subproject provides a home to modules contributed by external developers which may or may not move into the officially supported code base over time. A module in this subproject doesn't have to obey the rule of staying binary compatible between minor releases. Breaking API changes may be introduced in minor releases without notice as we refine and simplify based on your feedback. A module may be dropped in any release without prior deprecation. The Typesafe subscription does not cover support for these modules.

An instance filter that collapses instances with a common grouping ID value into a single instance. Useful for converting transactional data into a format that Weka's association rule learners can handle. IMPORTANT: assumes that the incoming batch of instances has been sorted on the grouping attribute. The values of nominal attributes are converted to indicator attributes. These can be either binary (with f and t values) or unary with missing values used to indicate absence. The later is Weka's old market basket format, which is useful for Apriori. Numeric attributes can be aggregated within groups by computing the average, sum, minimum or maximum.

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.

This subproject provides a home to modules contributed by external developers which may or may not move into the officially supported code base over time. A module in this subproject doesn't have to obey the rule of staying binary compatible between minor releases. Breaking API changes may be introduced in minor releases without notice as we refine and simplify based on your feedback. A module may be dropped in any release without prior deprecation. The Typesafe subscription does not cover support for these modules.

This subproject provides a home to modules contributed by external developers which may or may not move into the officially supported code base over time. A module in this subproject doesn't have to obey the rule of staying binary compatible between minor releases. Breaking API changes may be introduced in minor releases without notice as we refine and simplify based on your feedback. A module may be dropped in any release without prior deprecation. The Typesafe subscription does not cover support for these modules.

This subproject provides a home to modules contributed by external developers which may or may not move into the officially supported code base over time. A module in this subproject doesn't have to obey the rule of staying binary compatible between minor releases. Breaking API changes may be introduced in minor releases without notice as we refine and simplify based on your feedback. A module may be dropped in any release without prior deprecation. The Typesafe subscription does not cover support for these modules.

This subproject provides a home to modules contributed by external developers which may or may not move into the officially supported code base over time. A module in this subproject doesn't have to obey the rule of staying binary compatible between minor releases. Breaking API changes may be introduced in minor releases without notice as we refine and simplify based on your feedback. A module may be dropped in any release without prior deprecation. The Typesafe subscription does not cover support for these modules.

This subproject provides a home to modules contributed by external developers which may or may not move into the officially supported code base over time. A module in this subproject doesn't have to obey the rule of staying binary compatible between minor releases. Breaking API changes may be introduced in minor releases without notice as we refine and simplify based on your feedback. A module may be dropped in any release without prior deprecation. The Typesafe subscription does not cover support for these modules.

This subproject provides a home to modules contributed by external developers which may or may not move into the officially supported code base over time. A module in this subproject doesn't have to obey the rule of staying binary compatible between minor releases. Breaking API changes may be introduced in minor releases without notice as we refine and simplify based on your feedback. A module may be dropped in any release without prior deprecation. The Typesafe subscription does not cover support for these modules.

This subproject provides a home to modules contributed by external developers which may or may not move into the officially supported code base over time. A module in this subproject doesn't have to obey the rule of staying binary compatible between minor releases. Breaking API changes may be introduced in minor releases without notice as we refine and simplify based on your feedback. A module may be dropped in any release without prior deprecation. The Typesafe subscription does not cover support for these modules.