Class KiePMMLClassificationTable
- java.lang.Object
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- org.kie.pmml.commons.model.abstracts.AbstractKiePMMLComponent
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- org.kie.pmml.models.regression.model.AbstractKiePMMLTable
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- org.kie.pmml.models.regression.model.KiePMMLClassificationTable
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- All Implemented Interfaces:
Serializable
public final class KiePMMLClassificationTable extends AbstractKiePMMLTable
- See Also:
- Serialized Form
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Nested Class Summary
Nested Classes Modifier and Type Class Description static classKiePMMLClassificationTable.Builder
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Field Summary
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Fields inherited from class org.kie.pmml.models.regression.model.AbstractKiePMMLTable
categoricalFunctionMap, intercept, numericFunctionMap, predictorTermsFunctionMap, resultUpdater, targetCategory, targetField
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Method Summary
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Methods inherited from class org.kie.pmml.models.regression.model.AbstractKiePMMLTable
evaluateCategoricalPredictor, evaluateNumericWithExponent, evaluateNumericWithoutExponent, getCategoricalFunctionMap, getIntercept, getNumericFunctionMap, getPredictorTermsFunctionMap, getTargetCategory, getTargetField, updateCAUCHITResult, updateCLOGLOGResult, updateEXPResult, updateLOGITResult, updateNONEResult, updatePROBITResult, updateSOFTMAXResult
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Method Detail
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builder
public static KiePMMLClassificationTable.Builder builder(String name, List<org.kie.pmml.commons.model.KiePMMLExtension> extensions)
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evaluateRegression
public Object evaluateRegression(Map<String,Object> input, org.kie.pmml.api.runtime.PMMLRuntimeContext context)
- Overrides:
evaluateRegressionin classAbstractKiePMMLTable
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isBinary
public boolean isBinary()
A Classification is considered binary if it is of CATEGORICAL type and contains exactly two Regression tables- Returns:
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getRegressionNormalizationMethod
public REGRESSION_NORMALIZATION_METHOD getRegressionNormalizationMethod()
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getOpType
public org.kie.pmml.api.enums.OP_TYPE getOpType()
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getCategoryTableMap
public Map<String,KiePMMLRegressionTable> getCategoryTableMap()
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getProbabilityMap
public static LinkedHashMap<String,Double> getProbabilityMap(LinkedHashMap<String,Double> resultMap, DoubleUnaryOperator firstItemOperator, DoubleUnaryOperator secondItemOperator)
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getSOFTMAXProbabilityMap
public static LinkedHashMap<String,Double> getSOFTMAXProbabilityMap(LinkedHashMap<String,Double> resultMap)
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getSIMPLEMAXProbabilityMap
public static LinkedHashMap<String,Double> getSIMPLEMAXProbabilityMap(LinkedHashMap<String,Double> resultMap)
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getNONEProbabilityMap
public static LinkedHashMap<String,Double> getNONEProbabilityMap(LinkedHashMap<String,Double> resultMap)
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getNONEBinaryProbabilityMap
public static LinkedHashMap<String,Double> getNONEBinaryProbabilityMap(LinkedHashMap<String,Double> resultMap)
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getLOGITProbabilityMap
public static LinkedHashMap<String,Double> getLOGITProbabilityMap(LinkedHashMap<String,Double> resultMap)
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getPROBITProbabilityMap
public static LinkedHashMap<String,Double> getPROBITProbabilityMap(LinkedHashMap<String,Double> resultMap)
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getCLOGLOGProbabilityMap
public static LinkedHashMap<String,Double> getCLOGLOGProbabilityMap(LinkedHashMap<String,Double> resultMap)
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getCAUCHITProbabilityMap
public static LinkedHashMap<String,Double> getCAUCHITProbabilityMap(LinkedHashMap<String,Double> resultMap)
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