Packages

class LinearRegressionModel extends GeneralizedLinearModel with RegressionModel with Serializable with Saveable with PMMLExportable

Regression model trained using LinearRegression.

Annotations
@Since( "0.8.0" )
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Inherited
  1. LinearRegressionModel
  2. PMMLExportable
  3. Saveable
  4. RegressionModel
  5. GeneralizedLinearModel
  6. Serializable
  7. Serializable
  8. AnyRef
  9. Any
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Instance Constructors

  1. new LinearRegressionModel(weights: Vector, intercept: Double)

    weights

    Weights computed for every feature.

    intercept

    Intercept computed for this model.

    Annotations
    @Since( "1.1.0" )

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
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  3. final def ==(arg0: Any): Boolean
    Definition Classes
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  4. final def asInstanceOf[T0]: T0
    Definition Classes
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  5. def clone(): AnyRef
    Attributes
    protected[lang]
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    @throws( ... ) @native()
  6. final def eq(arg0: AnyRef): Boolean
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  7. def equals(arg0: Any): Boolean
    Definition Classes
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  8. def finalize(): Unit
    Attributes
    protected[lang]
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    @throws( classOf[java.lang.Throwable] )
  9. final def getClass(): Class[_]
    Definition Classes
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    Annotations
    @native()
  10. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  11. val intercept: Double
    Definition Classes
    LinearRegressionModelGeneralizedLinearModel
    Annotations
    @Since( "0.8.0" )
  12. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  13. final def ne(arg0: AnyRef): Boolean
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  14. final def notify(): Unit
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    @native()
  15. final def notifyAll(): Unit
    Definition Classes
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    @native()
  16. def predict(testData: JavaRDD[Vector]): JavaRDD[Double]

    Predict values for examples stored in a JavaRDD.

    Predict values for examples stored in a JavaRDD.

    testData

    JavaRDD representing data points to be predicted

    returns

    a JavaRDD[java.lang.Double] where each entry contains the corresponding prediction

    Definition Classes
    RegressionModel
    Annotations
    @Since( "1.0.0" )
  17. def predict(testData: Vector): Double

    Predict values for a single data point using the model trained.

    Predict values for a single data point using the model trained.

    testData

    array representing a single data point

    returns

    Double prediction from the trained model

    Definition Classes
    GeneralizedLinearModel
    Annotations
    @Since( "1.0.0" )
  18. def predict(testData: RDD[Vector]): RDD[Double]

    Predict values for the given data set using the model trained.

    Predict values for the given data set using the model trained.

    testData

    RDD representing data points to be predicted

    returns

    RDD[Double] where each entry contains the corresponding prediction

    Definition Classes
    GeneralizedLinearModel
    Annotations
    @Since( "1.0.0" )
  19. def predictPoint(dataMatrix: Vector, weightMatrix: Vector, intercept: Double): Double

    Predict the result given a data point and the weights learned.

    Predict the result given a data point and the weights learned.

    dataMatrix

    Row vector containing the features for this data point

    weightMatrix

    Column vector containing the weights of the model

    intercept

    Intercept of the model.

    Attributes
    protected
    Definition Classes
    LinearRegressionModelGeneralizedLinearModel
  20. def save(sc: SparkContext, path: String): Unit

    Save this model to the given path.

    Save this model to the given path.

    This saves:

    • human-readable (JSON) model metadata to path/metadata/
    • Parquet formatted data to path/data/

    The model may be loaded using Loader.load.

    sc

    Spark context used to save model data.

    path

    Path specifying the directory in which to save this model. If the directory already exists, this method throws an exception.

    Definition Classes
    LinearRegressionModelSaveable
    Annotations
    @Since( "1.3.0" )
  21. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  22. def toPMML(): String

    Export the model to a String in PMML format

    Export the model to a String in PMML format

    Definition Classes
    PMMLExportable
    Annotations
    @Since( "1.4.0" )
  23. def toPMML(outputStream: OutputStream): Unit

    Export the model to the OutputStream in PMML format

    Export the model to the OutputStream in PMML format

    Definition Classes
    PMMLExportable
    Annotations
    @Since( "1.4.0" )
  24. def toPMML(sc: SparkContext, path: String): Unit

    Export the model to a directory on a distributed file system in PMML format

    Export the model to a directory on a distributed file system in PMML format

    Definition Classes
    PMMLExportable
    Annotations
    @Since( "1.4.0" )
  25. def toPMML(localPath: String): Unit

    Export the model to a local file in PMML format

    Export the model to a local file in PMML format

    Definition Classes
    PMMLExportable
    Annotations
    @Since( "1.4.0" )
  26. def toString(): String

    Print a summary of the model.

    Print a summary of the model.

    Definition Classes
    GeneralizedLinearModel → AnyRef → Any
  27. final def wait(): Unit
    Definition Classes
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    @throws( ... )
  28. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
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    @throws( ... )
  29. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  30. val weights: Vector
    Definition Classes
    LinearRegressionModelGeneralizedLinearModel
    Annotations
    @Since( "1.0.0" )

Inherited from PMMLExportable

Inherited from Saveable

Inherited from RegressionModel

Inherited from GeneralizedLinearModel

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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