This is documentation for Orange 2.
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For the latest documentation, see Orange 3. Module rules implements supervised rule induction algorithms and rule-based classification methods. Rule induction is based on a comprehensive framework of components that can be Adult Orange finder Orange or replaced. For ease of use, the module already provides multiple variations of CN2 induction algorithm.Ladies Looking Nsa Reagan Tennessee 38368
The use of rule learning algorithms is consistent with a typical learner usage in Orange:. Classical CN2 inducer Clark and Niblett; Adult Orange finder Orange constructs a set of ordered rules. Classical CN2 classifier Clark and Niblett; that predicts a class from an ordered list of rules.
The classifier is usually constructed by CN2Learner. ValueOrange. Distribution or a tuple with both.
Unordered CN2 Clark and Boswell; induces a set of unordered rules. Learning rules is quite similar to learning in Orannge CN2, where the process of learning of rules is separated to learning rules for each class.
Unordered CN2 classifier Clark and Boswell; classifies an instance using a set of unordered rules. The classifier Adult Orange finder Orange typically constructed with CN2UnorderedLearner.
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The call has another optional argument that is used Adult Orange finder Orange tell the classifier to also return the rules that cover the given data instance. CN2-SD Lavrac et al. The classes described above are based on a more general framework that can be fine-tuned to specific needs by replacing individual components.
Here is an example:. In the example, we wanted to use a rule evaluator based on the m-estimate and set m to All other components, which are left unspecified, are provided by the learner at Adult Orange finder Orange training time and removed afterwards.
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Continuing Adult Orange finder Orange the example, assume that we wish to set a different validation function and a different beam width. Represents a single rule. Constructor arguments correspond to Adult Orange finder Orange first seven of the attributes from filter to quality below.
Rule condition; an instance of Orange. Filtertypically an instance of a class derived from Orange. A rule predicts the class by calling an embedded classifier that must be an instance of Classifiertypically ConstantClassifier.
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This classifier is called by the rule classifier, such as RuleClassifier. Learner that is used for constructing a classifier.
It must be an instance of Learnertypically MajorityLearner. Distribution of class in data instances covered by this rule Distribution. Data instances covered Adult Orange finder Orange Oange rule Table.
ID of the weight meta-attribute for the stored data instances int. Quality of the rule.
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Rules with higher quality are better float. Complexity of the rule floattypically the number of selectors conditions in the rule. Complexity is used for choosing between rules with the same quality; rules with lower complexity are preferred.
Filter passed tinder instances and store them in instances. Return True if the given instance matches the rule condition. A base rule induction learner.
The algorithm follows separate-and-conquer strategy, which has its origins in the AQ family of algorithms Fuernkranz J. Such algorithms search for the optimal findrr for the current training set, remove the covered training Adult Orange finder Orange separate and repeat the process conquer on the remaining data.
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An instance of DataStoppingCriteria that determines whether to continue the induction. An instance of Finder Odange learns a single rule. Default is BeamFinder. An Adult Orange finder Orange of StoppingCriteria that decides whether to use the induced rule or to discard it and Adult Orange finder Orange the induction.
If None default all rules are accepted. An instance of RuleCovererAndRemover that removes instances covered by the rule and returns remaining instances. Beam search for the best rule.
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This is the default finder for RuleLearner. Pseudo code of the algorithm is as follows. An instance of BeamInitializer that is used to construct the initial list of rules.Housewives Wants Real Sex Kingfisher Oklahoma
An instance of BeamRefiner that is used for refining the rules. Refined rule should cover a strict subset of instances covered by the given rule. An instance of BeamFilter that is used for filtering rules to trim the fijder beam.
Base class for rule evaluators that evaluate the quality of the rule based on the Adult Orange finder Orange instances they cover.Women To Fuck In Pool West Virginia
Base class for rule coverers and removers that, when invoked, remove instances covered by the rule and return remaining cinder. Orange Documentation v2. CN2Learner titanic Adult Orange finder Orange rule-base classifiers can have their rules printed out like this: Here is an example: RuleLearner learner. BeamFinder learner.
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Finder Beam search for the best rule. Distribution table.
These are: Table — data instances. Distribution — prior class distribution class Orange. Evaluator Weighted relative accuracy.
Evaluator class Orange. The CN2 Induction Algorithm. Machine Learning 3 4: Clark, Boswell. Rule Induction with CN2: Some Recent Improvements. Lavrac, Kavsek, Flach, Todorovski: Journal of Machine Learning Research 5: By default, entropy is used as a measure.
Instance — instance to be classified. GetValue or Orange.
GetProbabilities or Orange. By default, weighted relative accuracy is used.Baton Rouge Ri Lesbian Sex Sites
Instance — data instance. Table — a Oranye of Adult Orange finder Orange instances ref bool — if True defaultthe constructed table contains references to the original data instances; if Falsethe data is copied negate bool — inverts the selection.
Distribution — prior class distribution.