Package weka.classifiers.pmml.consumer
Class TreeModel
java.lang.Object
weka.classifiers.AbstractClassifier
weka.classifiers.pmml.consumer.PMMLClassifier
weka.classifiers.pmml.consumer.TreeModel
- All Implemented Interfaces:
Serializable,Cloneable,Classifier,BatchPredictor,CapabilitiesHandler,CapabilitiesIgnorer,CommandlineRunnable,Drawable,OptionHandler,PMMLModel,RevisionHandler
Class implementing import of PMML TreeModel. Can be used as a Weka classifier
for prediction (buildClassifier() raises and Exception).
- Version:
- $Revision: 10153 $;
- 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_DEFAULTFields inherited from interface weka.core.Drawable
BayesNet, Newick, NOT_DRAWABLE, TREE -
Constructor Summary
ConstructorsConstructorDescriptionTreeModel(Element model, Instances dataDictionary, MiningSchema miningSchema) -
Method Summary
Methods inherited from class weka.classifiers.pmml.consumer.PMMLClassifier
buildClassifier, done, getCreatorApplication, getDataDictionary, getFieldsMappingString, getLog, getMiningSchema, getPMMLVersion, mapToMiningSchema, setCreatorApplication, setLog, setPMMLVersionMethods 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
-
Constructor Details
-
TreeModel
public TreeModel(Element model, Instances dataDictionary, MiningSchema miningSchema) throws Exception - Throws:
Exception
-
-
Method Details
-
distributionForInstance
Classifies the given test instance. The instance has to belong to a dataset when it's being classified.- Specified by:
distributionForInstancein interfaceClassifier- Overrides:
distributionForInstancein 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
-
toString
-
graph
Description copied from interface:DrawableReturns a string that describes a graph representing the object. The string should be in XMLBIF ver. 0.3 format if the graph is a BayesNet, otherwise it should be in dotty format. -
getRevision
Description copied from class:AbstractClassifierReturns the revision string.- Specified by:
getRevisionin interfaceRevisionHandler- Overrides:
getRevisionin classAbstractClassifier- Returns:
- the revision
-
graphType
public int graphType()Description copied from interface:DrawableReturns the type of graph representing the object.
-