Instance Constructors
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new
H2ODeepLearningMOJOModel(uid: String)
Value Members
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
$[T](param: Param[T]): T
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final
def
==(arg0: Any): Boolean
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val
adaptiveRate: BooleanParam
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def
applyPredictionUdf(dataset: Dataset[_], udfConstructor: (Array[String]) ⇒ UserDefinedFunction): DataFrame
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def
applyPredictionUdfToFlatDataFrame(flatDataFrame: DataFrame, udfConstructor: (Array[String]) ⇒ UserDefinedFunction, inputs: Array[String]): DataFrame
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final
def
asInstanceOf[T0]: T0
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val
autoencoder: BooleanParam
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val
averageActivation: DoubleParam
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val
balanceClasses: BooleanParam
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def
booleanParam(name: String, doc: String): BooleanParam
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val
classificationStop: DoubleParam
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def
clone(): AnyRef
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final
val
convertInvalidNumbersToNa: BooleanParam
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final
val
convertUnknownCategoricalLevelsToNa: BooleanParam
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def
copy(extra: ParamMap): H2OMOJOModel
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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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final
val
detailedPredictionCol: Param[String]
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val
diagnostics: BooleanParam
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def
doubleArrayParam(name: String, doc: String): DoubleArrayParam
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def
doubleParam(name: String, doc: String): DoubleParam
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val
elasticAveraging: BooleanParam
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val
elasticAveragingMovingRate: DoubleParam
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val
elasticAveragingRegularization: DoubleParam
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val
epochs: DoubleParam
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val
epsilon: DoubleParam
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
explainParam(param: Param[_]): String
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def
explainParams(): String
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val
exportWeightsAndBiases: BooleanParam
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def
extractAnomalyPredictionColContent(): Column
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def
extractAutoEncoderPredictionColContent(): Column
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def
extractBinomialPredictionColContent(): Column
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def
extractClusteringPredictionColContent(): Column
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def
extractCoxPHPredictionColContent(): Column
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def
extractDimReductionSimplePredictionColContent(): Column
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def
extractMultinomialPredictionColContent(): Column
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def
extractOrdinalPredictionColContent(): Column
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final
def
extractParamMap(): ParamMap
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final
def
extractParamMap(extra: ParamMap): ParamMap
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def
extractPredictionColContent(): Column
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def
extractRegressionPredictionColContent(): Column
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def
extractWordEmbeddingPredictionColContent(): Column
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val
fastMode: BooleanParam
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final
val
featuresCols: StringArrayParam
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def
finalize(): Unit
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def
floatParam(name: String, doc: String): FloatParam
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val
forceLoadBalance: BooleanParam
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final
def
get[T](param: Param[T]): Option[T]
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def
getActivation(): String
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def
getAdaptiveRate(): Boolean
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def
getAnomalyPredictionColSchema(): Seq[StructField]
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def
getAnomalyPredictionSchema(): StructType
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def
getAnomalyPredictionUDF(): UserDefinedFunction
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def
getAucType(): String
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def
getAutoEncoderPredictionColSchema(): Seq[StructField]
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def
getAutoEncoderPredictionSchema(): StructType
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def
getAutoEncoderPredictionUDF(): UserDefinedFunction
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def
getAutoencoder(): Boolean
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def
getAverageActivation(): Double
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def
getBalanceClasses(): Boolean
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def
getBinomialPredictionColSchema(): Seq[StructField]
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def
getBinomialPredictionSchema(): StructType
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def
getBinomialPredictionUDF(): UserDefinedFunction
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def
getCategoricalEncoding(): String
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final
def
getClass(): Class[_]
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def
getClassSamplingFactors(): Array[Float]
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def
getClassificationStop(): Double
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def
getClusteringPredictionColSchema(): Seq[StructField]
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def
getClusteringPredictionSchema(): StructType
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def
getClusteringPredictionUDF(): UserDefinedFunction
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def
getContributionsSchema(model: EasyPredictModelWrapper): DataType
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def
getConvertInvalidNumbersToNa(): Boolean
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def
getConvertUnknownCategoricalLevelsToNa(): Boolean
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def
getCoxPHPredictionColSchema(): Seq[StructField]
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def
getCoxPHPredictionSchema(): StructType
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def
getCoxPHPredictionUDF(): UserDefinedFunction
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def
getCrossValidationMetrics(): Map[String, Double]
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def
getCurrentMetrics(): Map[String, Double]
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final
def
getDefault[T](param: Param[T]): Option[T]
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def
getDetailedPredictionCol(): String
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def
getDetailedPredictionColSchema(): Seq[StructField]
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def
getDiagnostics(): Boolean
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def
getDimReductionPredictionColSchema(): Seq[StructField]
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def
getDimReductionPredictionSchema(): StructType
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def
getDimReductionPredictionUDF(): UserDefinedFunction
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def
getDistribution(): String
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def
getDomainValues(): Map[String, Array[String]]
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def
getElasticAveraging(): Boolean
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def
getElasticAveragingMovingRate(): Double
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def
getElasticAveragingRegularization(): Double
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def
getEpochs(): Double
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def
getEpsilon(): Double
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def
getExportCheckpointsDir(): String
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def
getExportWeightsAndBiases(): Boolean
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def
getFastMode(): Boolean
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def
getFeatureTypes(): Map[String, String]
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def
getFeaturesCols(): Array[String]
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def
getFoldAssignment(): String
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def
getFoldCol(): String
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def
getForceLoadBalance(): Boolean
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def
getHidden(): Array[Int]
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def
getHiddenDropoutRatios(): Array[Double]
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def
getHuberAlpha(): Double
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def
getIgnoreConstCols(): Boolean
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def
getIgnoredCols(): Array[String]
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def
getInitialWeightDistribution(): String
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def
getInitialWeightScale(): Double
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def
getInputDropoutRatio(): Double
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def
getKeepCrossValidationFoldAssignment(): Boolean
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def
getKeepCrossValidationModels(): Boolean
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def
getKeepCrossValidationPredictions(): Boolean
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def
getL1(): Double
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def
getL2(): Double
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def
getLabelCol(): String
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def
getLoss(): String
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def
getMaxAfterBalanceSize(): Float
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def
getMaxCategoricalFeatures(): Int
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def
getMaxRuntimeSecs(): Double
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def
getMaxW2(): Float
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def
getMiniBatchSize(): Int
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def
getMissingValuesHandling(): String
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def
getModelCategory(): String
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def
getModelDetails(): String
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def
getMojo(): File
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def
getMomentumRamp(): Double
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def
getMomentumStable(): Double
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def
getMomentumStart(): Double
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def
getMultinomialPredictionColSchema(): Seq[StructField]
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def
getMultinomialPredictionSchema(): StructType
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def
getMultinomialPredictionUDF(): UserDefinedFunction
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def
getNamedMojoOutputColumns(): Boolean
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def
getNesterovAcceleratedGradient(): Boolean
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def
getNfolds(): Int
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def
getOffsetCol(): String
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final
def
getOrDefault[T](param: Param[T]): T
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def
getOrdinalPredictionColSchema(): Seq[StructField]
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def
getOrdinalPredictionSchema(): StructType
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def
getOrdinalPredictionUDF(): UserDefinedFunction
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def
getOverwriteWithBestModel(): Boolean
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def
getParam(paramName: String): Param[Any]
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def
getPredictionCol(): String
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def
getPredictionColSchema(): Seq[StructField]
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def
getPredictionSchema(): StructType
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def
getPredictionUDF(): UserDefinedFunction
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def
getQuantileAlpha(): Double
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def
getQuietMode(): Boolean
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def
getRate(): Double
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def
getRateAnnealing(): Double
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def
getRateDecay(): Double
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def
getRegressionPredictionColSchema(): Seq[StructField]
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def
getRegressionPredictionSchema(): StructType
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def
getRegressionPredictionUDF(): UserDefinedFunction
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def
getRegressionStop(): Double
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def
getRelevantColumnNames(flatDataFrame: DataFrame, inputs: Array[String]): Array[String]
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def
getReplicateTrainingData(): Boolean
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def
getReproducible(): Boolean
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def
getRho(): Double
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def
getScoreDutyCycle(): Double
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def
getScoreEachIteration(): Boolean
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def
getScoreInterval(): Double
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def
getScoreTrainingSamples(): Long
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def
getScoreValidationSamples(): Long
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def
getScoreValidationSampling(): String
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def
getSeed(): Long
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def
getShuffleTrainingData(): Boolean
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def
getSingleNodeMode(): Boolean
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def
getSparse(): Boolean
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def
getSparsityBeta(): Double
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def
getStageProbabilitiesSchema(model: EasyPredictModelWrapper): DataType
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def
getStandardize(): Boolean
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def
getStoppingMetric(): String
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def
getStoppingRounds(): Int
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def
getStoppingTolerance(): Double
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def
getTargetRatioCommToComp(): Double
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def
getTrainSamplesPerIteration(): Long
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def
getTrainingMetrics(): Map[String, Double]
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def
getTrainingParams(): Map[String, String]
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def
getTweediePower(): Double
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def
getUseAllFactorLevels(): Boolean
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def
getValidationMetrics(): Map[String, Double]
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def
getVariableImportances(): Boolean
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def
getWeightCol(): String
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def
getWithContributions(): Boolean
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def
getWithDetailedPredictionCol(): Boolean
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def
getWithLeafNodeAssignments(): Boolean
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def
getWithStageResults(): Boolean
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def
getWordEmbeddingPredictionColSchema(): Seq[StructField]
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def
getWordEmbeddingPredictionSchema(): StructType
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def
getWordEmbeddingPredictionUDF(): UserDefinedFunction
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final
def
hasDefault[T](param: Param[T]): Boolean
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def
hasParam(paramName: String): Boolean
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def
hasParent: Boolean
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def
hashCode(): Int
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val
hidden: IntArrayParam
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val
huberAlpha: DoubleParam
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val
ignoreConstCols: BooleanParam
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val
initialWeightScale: DoubleParam
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def
initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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def
initializeLogIfNecessary(isInterpreter: Boolean): Unit
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def
inputColumnNames: Array[String]
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val
inputDropoutRatio: DoubleParam
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def
intArrayParam(name: String, doc: String): IntArrayParam
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def
intParam(name: String, doc: String): IntParam
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final
def
isDefined(param: Param[_]): Boolean
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final
def
isInstanceOf[T0]: Boolean
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final
def
isSet(param: Param[_]): Boolean
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def
isTraceEnabled(): Boolean
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val
keepCrossValidationFoldAssignment: BooleanParam
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val
keepCrossValidationModels: BooleanParam
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val
keepCrossValidationPredictions: BooleanParam
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val
l1: DoubleParam
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val
l2: DoubleParam
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val
labelCol: Param[String]
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def
log: Logger
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def
logDebug(msg: ⇒ String, throwable: Throwable): Unit
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def
logDebug(msg: ⇒ String): Unit
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def
logError(msg: ⇒ String, throwable: Throwable): Unit
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def
logError(msg: ⇒ String): Unit
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def
logInfo(msg: ⇒ String, throwable: Throwable): Unit
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def
logInfo(msg: ⇒ String): Unit
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def
logName: String
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def
logTrace(msg: ⇒ String, throwable: Throwable): Unit
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def
logTrace(msg: ⇒ String): Unit
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def
logWarning(msg: ⇒ String, throwable: Throwable): Unit
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def
logWarning(msg: ⇒ String): Unit
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def
longParam(name: String, doc: String): LongParam
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val
maxAfterBalanceSize: FloatParam
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val
maxCategoricalFeatures: IntParam
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val
maxRuntimeSecs: DoubleParam
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val
maxW2: FloatParam
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val
miniBatchSize: IntParam
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val
momentumRamp: DoubleParam
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val
momentumStable: DoubleParam
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val
momentumStart: DoubleParam
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final
val
namedMojoOutputColumns: Param[Boolean]
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final
def
ne(arg0: AnyRef): Boolean
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val
nesterovAcceleratedGradient: BooleanParam
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val
nfolds: IntParam
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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def
nullableDoubleArrayArrayParam(name: String, doc: String): NullableDoubleArrayArrayParam
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def
nullableDoubleArrayParam(name: String, doc: String): NullableDoubleArrayParam
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def
nullableFloatArrayParam(name: String, doc: String): NullableFloatArrayParam
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def
nullableIntArrayParam(name: String, doc: String): NullableIntArrayParam
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def
nullableStringArrayArrayParam(name: String, doc: String): NullableStringArrayArrayParam
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def
nullableStringArrayParam(name: String, doc: String): NullableStringArrayParam
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def
nullableStringPairArrayParam(name: String, doc: String): NullableStringPairArrayParam
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def
nullableStringParam(name: String, doc: String): NullableStringParam
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def
outputColumnName: String
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val
overwriteWithBestModel: BooleanParam
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def
param[T](name: String, doc: String): Param[T]
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lazy val
params: Array[Param[_]]
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final
val
predictionCol: Param[String]
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val
quantileAlpha: DoubleParam
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val
quietMode: BooleanParam
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val
rate: DoubleParam
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val
rateAnnealing: DoubleParam
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val
rateDecay: DoubleParam
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val
regressionStop: DoubleParam
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val
replicateTrainingData: BooleanParam
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val
reproducible: BooleanParam
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val
rho: DoubleParam
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def
save(path: String): Unit
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val
scoreDutyCycle: DoubleParam
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val
scoreEachIteration: BooleanParam
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val
scoreInterval: DoubleParam
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val
scoreTrainingSamples: LongParam
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val
scoreValidationSamples: LongParam
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val
seed: LongParam
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final
def
set(paramPair: ParamPair[_]): H2ODeepLearningMOJOModel.this.type
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final
def
set(param: String, value: Any): H2ODeepLearningMOJOModel.this.type
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final
def
set[T](param: Param[T], value: T): H2ODeepLearningMOJOModel.this.type
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final
def
setDefault(paramPairs: ParamPair[_]*): H2ODeepLearningMOJOModel.this.type
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final
def
setDefault[T](param: Param[T], value: T): H2ODeepLearningMOJOModel.this.type
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def
setMojo(mojo: InputStream, mojoName: String): H2ODeepLearningMOJOModel.this.type
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-
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val
shuffleTrainingData: BooleanParam
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val
singleNodeMode: BooleanParam
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val
sparse: BooleanParam
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val
sparsityBeta: DoubleParam
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val
standardize: BooleanParam
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val
stoppingRounds: IntParam
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val
stoppingTolerance: DoubleParam
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def
stringArrayParam(name: String, doc: String): StringArrayParam
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def
stringParam(name: String, doc: String): Param[String]
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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val
targetRatioCommToComp: DoubleParam
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def
toString(): String
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val
trainSamplesPerIteration: LongParam
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-
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def
transform(dataset: Dataset[_]): DataFrame
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def
transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
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def
transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
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def
transformSchema(schema: StructType): StructType
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def
transformSchema(schema: StructType, logging: Boolean): StructType
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val
tweediePower: DoubleParam
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val
uid: String
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val
useAllFactorLevels: BooleanParam
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val
variableImportances: BooleanParam
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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final
val
withContributions: BooleanParam
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final
val
withDetailedPredictionCol: BooleanParam
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final
val
withLeafNodeAssignments: BooleanParam
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final
val
withStageResults: BooleanParam
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def
write: MLWriter
Inherited from MLWritable
Inherited from HasMojo
Inherited from Logging
Inherited from Transformer
Inherited from PipelineStage
Inherited from Logging
Inherited from Params
Inherited from Serializable
Inherited from Serializable
Inherited from Identifiable
Inherited from AnyRef
Inherited from Any