BinaryClassificationModelSelector

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1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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4. object Defaults extends ModelDefaults[BinaryClassificationModelsToTry] with Product with Serializable

   

   Default model types and model parameters for problem type

5. def addDefaultEvaluators(evaluators: Seq[OpEvaluatorBase[_ <: EvaluationMetrics]], defaultEvaluators: Seq[OpEvaluatorBase[_ <: EvaluationMetrics]]): Seq[OpEvaluatorBase[_ <: EvaluationMetrics]]

   

   Attributes

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   ModelSelectorFactory

6. def apply(): ModelSelector[ModelType, EstimatorType]

   

   Creates a new Binary Classification Model Selector with a Cross Validation

7. final def asInstanceOf[T0]: T0

   

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8. def clone(): AnyRef

   

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9. final def eq(arg0: AnyRef): Boolean

   

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10. def equals(arg0: Any): Boolean

    

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11. def finalize(): Unit

    

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12. final def getClass(): Class[_]

    

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13. final def isInstanceOf[T0]: Boolean

    

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14. final def ne(arg0: AnyRef): Boolean

    

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15. final def notify(): Unit

    

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16. final def notifyAll(): Unit

    

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17. def selector(validator: OpValidator[ModelType, EstimatorType], splitter: Option[Splitter], trainTestEvaluators: Seq[OpEvaluatorBase[_ <: EvaluationMetrics]], modelTypesToUse: Seq[ModelsToTry], modelsAndParameters: Seq[(EstimatorType, Array[ParamMap])], modelDefaults: ModelDefaults[_ <: ModelsToTry]): ModelSelector[ModelType, EstimatorType]

    

    Create the model selector for specified interface

    Create the model selector for specified interface

    validator

    training split of cross validator

    splitter

    data prep class

    trainTestEvaluators

    evaluation to do on data

    modelTypesToUse

    list of models to use

    modelsAndParameters

    sequence of models and parameters to explore

    modelDefaults

    default model types and model parameters for problem type

    returns

    model selector with these settings

    Attributes

    protected

    Definition Classes

    ModelSelectorFactory

18. final def synchronized[T0](arg0: ⇒ T0): T0

    

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19. final def wait(): Unit

    

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20. final def wait(arg0: Long, arg1: Int): Unit

    

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21. final def wait(arg0: Long): Unit

    

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22. def withCrossValidation(splitter: Option[Splitter] = Option(DataSplitter()), numFolds: Int = ValidatorParamDefaults.NumFolds, validationMetric: OpBinaryClassificationEvaluatorBase[_ <: EvaluationMetrics] = ..., trainTestEvaluators: Seq[OpBinaryClassificationEvaluatorBase[_ <: EvaluationMetrics]] = ..., seed: Long = ValidatorParamDefaults.Seed, stratify: Boolean = ValidatorParamDefaults.Stratify, parallelism: Int = ValidatorParamDefaults.Parallelism, modelTypesToUse: Seq[BinaryClassificationModelsToTry] = Defaults.modelTypesToUse, modelsAndParameters: Seq[(EstimatorType, Array[ParamMap])] = Seq.empty, maxWait: Duration = ValidatorParamDefaults.MaxWait): ModelSelector[ModelType, EstimatorType]

    

    Creates a new Binary Classification Model Selector with a Cross Validation

    Creates a new Binary Classification Model Selector with a Cross Validation

    splitter

    instance that will balance and split the data

    numFolds

    number of folds for cross validation (>= 2)

    validationMetric

    metric name in evaluation: AuROC or AuPR

    trainTestEvaluators

    List of evaluators applied on training + holdout data for evaluation. Default is the standard OpBinaryClassificationEvaluator and OpBinScoreEvaluator.

    seed

    random seed

    stratify

    whether or not stratify cross validation. Caution : setting that param to true might impact the runtime

    parallelism

    level of parallelism used to schedule a number of models to be trained/evaluated so that the jobs can be run concurrently

    modelTypesToUse

    list of model types to run grid search on must from supported types in BinaryClassificationModelsToTry (OpLogisticRegression, OpRandomForestClassifier, OpGBTClassifier, OpLinearSVC, OpDecisionTreeClassifier, OpNaiveBayes)

    modelsAndParameters

    pass in an explicit list pairs of estimators and the accompanying hyperparameters to for model selection Seq[(EstimatorType, Array[ParamMap])] where Estimator type must be an Estimator that takes in a label (RealNN) and features (OPVector) and returns a prediction (Prediction)

    maxWait

    maximum allowable time to wait for a model to finish running (default is 1 day)

    returns

    Classification Model Selector with a Cross Validation

23. def withTrainValidationSplit(splitter: Option[Splitter] = Option(DataSplitter()), trainRatio: Double = ValidatorParamDefaults.TrainRatio, validationMetric: OpBinaryClassificationEvaluatorBase[_ <: EvaluationMetrics] = ..., trainTestEvaluators: Seq[OpBinaryClassificationEvaluatorBase[_ <: EvaluationMetrics]] = ..., seed: Long = ValidatorParamDefaults.Seed, stratify: Boolean = ValidatorParamDefaults.Stratify, parallelism: Int = ValidatorParamDefaults.Parallelism, modelTypesToUse: Seq[BinaryClassificationModelsToTry] = Defaults.modelTypesToUse, modelsAndParameters: Seq[(EstimatorType, Array[ParamMap])] = Seq.empty, maxWait: Duration = ValidatorParamDefaults.MaxWait): ModelSelector[ModelType, EstimatorType]

    

    Creates a new Binary Classification Model Selector with a Train Validation Split

    Creates a new Binary Classification Model Selector with a Train Validation Split

    splitter

    instance that will balance and split the data

    trainRatio

    ratio between training set and validation set (>= 0 && <= 1)

    validationMetric

    metric name in evaluation: AuROC or AuPR

    trainTestEvaluators

    List of evaluators applied on training + holdout data for evaluation. Default is the standard OpBinaryClassificationEvaluator and OpBinScoreEvaluator.

    seed

    random seed

    stratify

    whether or not stratify train validation split. Caution : setting that param to true might impact the runtime

    parallelism

    level of parallelism used to schedule a number of models to be trained/evaluated so that the jobs can be run concurrently

    modelTypesToUse

    list of model types to run grid search on must from supported types in BinaryClassificationModelsToTry (OpLogisticRegression, OpRandomForestClassifier, OpGBTClassifier, OpLinearSVC, OpDecisionTreeClassifier, OpNaiveBayes)

    modelsAndParameters

    pass in an explicit list pairs of estimators and the accompanying hyperparameters to for model selection Seq[(EstimatorType, Array[ParamMap])] where Estimator type must be an Estimator that takes in a label (RealNN) and features (OPVector) and returns a prediction (Prediction)

    maxWait

    maximum allowable time to wait for a model to finish running (default is 1 day)

    returns

    Classification Model Selector with a Train Validation Split