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Class Summary  

ConjunctiveRule  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. 
DecisionTable  Class for building and using a simple decision table majority classifier. For more information see: Ron Kohavi: The Power of Decision Tables. 
DecisionTableHashKey  Class providing hash table keys for DecisionTable 
DTNB  Class for building and using a decision table/naive bayes hybrid classifier. 
FURIA  FURIA: Fuzzy Unordered Rule Induction Algorithm Details please see: Jens Christian Huehn, Eyke Huellermeier (2009). 
JRip  This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which was proposed by William W. 
M5Rules  Generates a decision list for regression problems using separateandconquer. 
NNge  Nearestneighborlike algorithm using nonnested generalized exemplars (which are hyperrectangles that can be viewed as ifthen rules). 
OLM  This class is an implementation of the Ordinal Learning Method (OLM). Further information regarding the algorithm and variants can be found in: Arie BenDavid (1992). 
OneR  Class for building and using a 1R classifier; in other words, uses the minimumerror attribute for prediction, discretizing numeric attributes. 
PART  Class for generating a PART decision list. 
Prism  Class for building and using a PRISM rule set for classification. 
Ridor  An implementation of a RIppleDOwn Rule learner. It generates a default rule first and then the exceptions for the default rule with the least (weighted) error rate. 
Rule  Abstract class of generic rule 
RuleStats  This class implements the statistics functions used in the propositional rule learner, from the simpler ones like count of true/false positive/negatives, filter data based on the ruleset, etc. 
ZeroR  Class for building and using a 0R classifier. 


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