keystoneml.nodes.learning

LogisticRegressionEstimator

case class LogisticRegressionEstimator[T <: Vector[Double]](numClasses: Int, regParam: Double = 0, numIters: Int = 100, convergenceTol: Double = 1.0E-4, numFeatures: Int = -1)(implicit evidence$1: ClassTag[T]) extends LabelEstimator[T, Double, Int] with Product with Serializable

A LabelEstimator which learns a Logistic Regression model from training data. Currently does so using LBFG-S

numClasses

The number of classes

numIters

The max number of iterations to use. Default 100

convergenceTol

Set the convergence tolerance of iterations for the optimizer. Default 1E-4.

Linear Supertypes
Product, Equals, LabelEstimator[T, Double, Int], EstimatorOperator, Serializable, Serializable, Operator, AnyRef, Any
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  1. LogisticRegressionEstimator
  2. Product
  3. Equals
  4. LabelEstimator
  5. EstimatorOperator
  6. Serializable
  7. Serializable
  8. Operator
  9. AnyRef
  10. Any
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Instance Constructors

  1. new LogisticRegressionEstimator(numClasses: Int, regParam: Double = 0, numIters: Int = 100, convergenceTol: Double = 1.0E-4, numFeatures: Int = -1)(implicit arg0: ClassTag[T])

    numClasses

    The number of classes

    numIters

    The max number of iterations to use. Default 100

    convergenceTol

    Set the convergence tolerance of iterations for the optimizer. Default 1E-4.

Value Members

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

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  3. final def ##(): Int

    Definition Classes
    AnyRef → Any

  4. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  6. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  7. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  8. val convergenceTol: Double

    Set the convergence tolerance of iterations for the optimizer.

    Set the convergence tolerance of iterations for the optimizer. Default 1E-4.

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

    Definition Classes
    AnyRef

  10. def execute(deps: Seq[Expression]): TransformerExpression

    Definition Classes
    EstimatorOperator → Operator

  11. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  12. def fit(in: RDD[T], labels: RDD[Int]): LogisticRegressionModel[T]

    The type-safe method that ML developers need to implement when writing new Estimators.

    The type-safe method that ML developers need to implement when writing new Estimators.

    labels

    The estimator's training labels

    returns

    A new transformer

    Definition Classes
    LogisticRegressionEstimator → LabelEstimator

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

    Definition Classes
    AnyRef → Any

  14. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  15. def label: String

    Definition Classes
    Operator

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

    Definition Classes
    AnyRef

  17. final def notify(): Unit

    Definition Classes
    AnyRef

  18. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  19. val numClasses: Int

    The number of classes

  20. val numFeatures: Int

  21. val numIters: Int

    The max number of iterations to use.

    The max number of iterations to use. Default 100

  22. val regParam: Double

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

    Definition Classes
    AnyRef

  24. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  27. final def withData(data: PipelineDataset[T], labels: PipelineDataset[Int]): Pipeline[T, Double]

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    data

    The training data

    labels

    The training labels

    returns

    A pipeline that fits this label estimator and applies the result to inputs.

    Definition Classes
    LabelEstimator

  28. final def withData(data: RDD[T], labels: RDD[Int]): Pipeline[T, Double]

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    data

    The training data

    labels

    The training labels

    returns

    A pipeline that fits this label estimator and applies the result to inputs.

    Definition Classes
    LabelEstimator

  29. final def withData(data: PipelineDataset[T], labels: RDD[Int]): Pipeline[T, Double]

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    data

    The training data

    labels

    The training labels

    returns

    A pipeline that fits this label estimator and applies the result to inputs.

    Definition Classes
    LabelEstimator

  30. final def withData(data: RDD[T], labels: PipelineDataset[Int]): Pipeline[T, Double]

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    data

    The training data

    labels

    The training labels

    returns

    A pipeline that fits this label estimator and applies the result to inputs.

    Definition Classes
    LabelEstimator

Inherited from Product

Inherited from Equals

Inherited from LabelEstimator[T, Double, Int]

Inherited from EstimatorOperator

Inherited from Serializable

Inherited from Serializable

Inherited from Operator

Inherited from AnyRef

Inherited from Any

Ungrouped