c
org.apache.spark.ml.classification
DummyClassificationModel
Companion object DummyClassificationModel
class DummyClassificationModel extends ProbabilisticClassificationModel[Vector, DummyClassificationModel] with DummyClassifierParams with MLWritable
- Source
- DummyClassifier.scala
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- DummyClassificationModel
- MLWritable
- DummyClassifierParams
- HasTol
- ProbabilisticClassificationModel
- ProbabilisticClassifierParams
- HasThresholds
- HasProbabilityCol
- ClassificationModel
- ClassifierParams
- HasRawPredictionCol
- PredictionModel
- PredictorParams
- HasPredictionCol
- HasFeaturesCol
- HasLabelCol
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- final def asInstanceOf[T0]: T0
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- final def clear(param: Param[_]): DummyClassificationModel.this.type
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- def clone(): AnyRef
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- protected[lang]
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- @throws(classOf[java.lang.CloneNotSupportedException]) @native()
- val constant: Param[Double]
param for the constant predicted by the predictor
param for the constant predicted by the predictor
- Definition Classes
- DummyClassifierParams
- def copy(extra: ParamMap): DummyClassificationModel
- Definition Classes
- DummyClassificationModel → Model → Transformer → PipelineStage → Params
- def copyValues[T <: Params](to: T, extra: ParamMap): T
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- final def defaultCopy[T <: Params](extra: ParamMap): T
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- def explainParam(param: Param[_]): String
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- def explainParams(): String
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- Params
- def extractInstances(dataset: Dataset[_], numClasses: Int): RDD[Instance]
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- protected
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- ClassifierParams
- def extractInstances(dataset: Dataset[_], validateInstance: (Instance) => Unit): RDD[Instance]
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- protected
- Definition Classes
- PredictorParams
- def extractInstances(dataset: Dataset[_]): RDD[Instance]
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- PredictorParams
- final def extractParamMap(): ParamMap
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- final def extractParamMap(extra: ParamMap): ParamMap
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- Params
- final val featuresCol: Param[String]
- Definition Classes
- HasFeaturesCol
- def featuresDataType: DataType
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- PredictionModel
- def finalize(): Unit
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- def getConstant: Double
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- DummyClassifierParams
- final def getDefault[T](param: Param[T]): Option[T]
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- final def getFeaturesCol: String
- Definition Classes
- HasFeaturesCol
- final def getLabelCol: String
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- HasLabelCol
- final def getOrDefault[T](param: Param[T]): T
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- def getParam(paramName: String): Param[Any]
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- final def getPredictionCol: String
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- HasPredictionCol
- final def getProbabilityCol: String
- Definition Classes
- HasProbabilityCol
- final def getRawPredictionCol: String
- Definition Classes
- HasRawPredictionCol
- def getStrategy: String
- Definition Classes
- DummyClassifierParams
- def getThresholds: Array[Double]
- Definition Classes
- HasThresholds
- final def getTol: Double
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- HasTol
- final def hasDefault[T](param: Param[T]): Boolean
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- @native()
- val numClasses: Int
- Definition Classes
- DummyClassificationModel → ClassificationModel
- def numFeatures: Int
- Definition Classes
- PredictionModel
- Annotations
- @Since("1.6.0")
- lazy val params: Array[Param[_]]
- Definition Classes
- Params
- var parent: Estimator[DummyClassificationModel]
- Definition Classes
- Model
- def predict(features: Vector): Double
- Definition Classes
- ClassificationModel → PredictionModel
- def predictProbability(features: Vector): Vector
- Definition Classes
- ProbabilisticClassificationModel
- Annotations
- @Since("3.0.0")
- def predictRaw(features: Vector): Vector
- Definition Classes
- DummyClassificationModel → ClassificationModel
- final val predictionCol: Param[String]
- Definition Classes
- HasPredictionCol
- val probability: Vector
- def probability2prediction(probability: Vector): Double
- Attributes
- protected
- Definition Classes
- ProbabilisticClassificationModel
- final val probabilityCol: Param[String]
- Definition Classes
- HasProbabilityCol
- def raw2prediction(rawPrediction: Vector): Double
- Attributes
- protected
- Definition Classes
- ProbabilisticClassificationModel → ClassificationModel
- def raw2probability(rawPrediction: Vector): Vector
- Attributes
- protected
- Definition Classes
- ProbabilisticClassificationModel
- def raw2probabilityInPlace(rawPrediction: Vector): Vector
- Attributes
- protected
- Definition Classes
- DummyClassificationModel → ProbabilisticClassificationModel
- val rawPrediction: Vector
- final val rawPredictionCol: Param[String]
- Definition Classes
- HasRawPredictionCol
- def save(path: String): Unit
- Definition Classes
- MLWritable
- Annotations
- @Since("1.6.0") @throws("If the input path already exists but overwrite is not enabled.")
- final def set(paramPair: ParamPair[_]): DummyClassificationModel.this.type
- Attributes
- protected
- Definition Classes
- Params
- final def set(param: String, value: Any): DummyClassificationModel.this.type
- Attributes
- protected
- Definition Classes
- Params
- final def set[T](param: Param[T], value: T): DummyClassificationModel.this.type
- Definition Classes
- Params
- def setConstant(value: Double): DummyClassificationModel.this.type
- final def setDefault(paramPairs: ParamPair[_]*): DummyClassificationModel.this.type
- Attributes
- protected
- Definition Classes
- Params
- final def setDefault[T](param: Param[T], value: T): DummyClassificationModel.this.type
- Attributes
- protected
- Definition Classes
- Params
- def setFeaturesCol(value: String): DummyClassificationModel
- Definition Classes
- PredictionModel
- def setParent(parent: Estimator[DummyClassificationModel]): DummyClassificationModel
- Definition Classes
- Model
- def setPredictionCol(value: String): DummyClassificationModel
- Definition Classes
- PredictionModel
- def setProbabilityCol(value: String): DummyClassificationModel
- Definition Classes
- ProbabilisticClassificationModel
- def setRawPredictionCol(value: String): DummyClassificationModel
- Definition Classes
- ClassificationModel
- def setStrategy(value: String): DummyClassificationModel.this.type
- def setThresholds(value: Array[Double]): DummyClassificationModel
- Definition Classes
- ProbabilisticClassificationModel
- def setTol(value: Double): DummyClassificationModel.this.type
- val strategy: Param[String]
strategy to use to generate predictions.
strategy to use to generate predictions. (case-insensitive) Supported: "uniform", "prior", "constant". (default = uniform)
- Definition Classes
- DummyClassifierParams
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
- AnyRef
- val thresholds: DoubleArrayParam
- Definition Classes
- HasThresholds
- def toString(): String
- Definition Classes
- DummyClassificationModel → Identifiable → AnyRef → Any
- final val tol: DoubleParam
- Definition Classes
- HasTol
- def transform(dataset: Dataset[_]): DataFrame
- Definition Classes
- ProbabilisticClassificationModel → ClassificationModel → PredictionModel → Transformer
- def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
- Definition Classes
- Transformer
- Annotations
- @Since("2.0.0")
- def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
- Definition Classes
- Transformer
- Annotations
- @Since("2.0.0") @varargs()
- final def transformImpl(dataset: Dataset[_]): DataFrame
- Definition Classes
- ClassificationModel → PredictionModel
- def transformSchema(schema: StructType): StructType
- Definition Classes
- ProbabilisticClassificationModel → ClassificationModel → PredictionModel → PipelineStage
- def transformSchema(schema: StructType, logging: Boolean): StructType
- Attributes
- protected
- Definition Classes
- PipelineStage
- Annotations
- @DeveloperApi()
- val uid: String
- Definition Classes
- DummyClassificationModel → Identifiable
- def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType
- Attributes
- protected
- Definition Classes
- ProbabilisticClassifierParams → ClassifierParams → PredictorParams
- final def wait(): Unit
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- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long, arg1: Int): Unit
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- final def wait(arg0: Long): Unit
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- @throws(classOf[java.lang.InterruptedException]) @native()
- def write: MLWriter
- Definition Classes
- DummyClassificationModel → MLWritable