Package weka.classifiers.pmml.consumer
Class Regression
java.lang.Object
weka.classifiers.AbstractClassifier
weka.classifiers.pmml.consumer.PMMLClassifier
weka.classifiers.pmml.consumer.Regression
- All Implemented Interfaces:
Serializable
,Cloneable
,Classifier
,BatchPredictor
,CapabilitiesHandler
,CapabilitiesIgnorer
,CommandlineRunnable
,OptionHandler
,PMMLModel
,RevisionHandler
Class implementing import of PMML Regression model. Can be
used as a Weka classifier for prediction (buildClassifier()
raises an Exception).
- Version:
- $Revision: 8034 $
- Author:
- Mark Hall (mhall{[at]}pentaho{[dot]}com
- See Also:
-
Field Summary
Fields inherited from class weka.classifiers.AbstractClassifier
BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT
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Constructor Summary
ConstructorDescriptionRegression
(Element model, Instances dataDictionary, MiningSchema miningSchema) Constructs a new PMML Regression. -
Method Summary
Modifier and TypeMethodDescriptiondouble[]
Classifies the given test instance.Returns the revision string.toString()
Return a textual description of this Regression model.Methods inherited from class weka.classifiers.pmml.consumer.PMMLClassifier
buildClassifier, done, getCreatorApplication, getDataDictionary, getFieldsMappingString, getLog, getMiningSchema, getPMMLVersion, mapToMiningSchema, setCreatorApplication, setLog, setPMMLVersion
Methods inherited from class weka.classifiers.AbstractClassifier
batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getCapabilities, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, getOptions, implementsMoreEfficientBatchPrediction, listOptions, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces, setOptions
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Constructor Details
-
Regression
public Regression(Element model, Instances dataDictionary, MiningSchema miningSchema) throws Exception Constructs a new PMML Regression.- Parameters:
model
- theElement
containing the regression modeldataDictionary
- the data dictionary as an Instances objectminingSchema
- the mining schema- Throws:
Exception
- if there is a problem constructing this Regression
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Method Details
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toString
Return a textual description of this Regression model. -
distributionForInstance
Classifies the given test instance. The instance has to belong to a dataset when it's being classified.- Specified by:
distributionForInstance
in interfaceClassifier
- Overrides:
distributionForInstance
in classAbstractClassifier
- Parameters:
inst
- the instance to be classified- Returns:
- the predicted most likely class for the instance or Utils.missingValue() if no prediction is made
- Throws:
Exception
- if an error occurred during the prediction
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getRevision
Description copied from class:AbstractClassifier
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classAbstractClassifier
- Returns:
- the revision
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