Packages

class SVMModel extends GeneralizedLinearModel with ClassificationModel with Serializable with Saveable with PMMLExportable

Model for Support Vector Machines (SVMs).

Annotations
@Since( "0.8.0" )
Source
SVM.scala
Linear Supertypes
PMMLExportable, Saveable, ClassificationModel, GeneralizedLinearModel, Serializable, Serializable, AnyRef, Any
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Inherited
  1. SVMModel
  2. PMMLExportable
  3. Saveable
  4. ClassificationModel
  5. GeneralizedLinearModel
  6. Serializable
  7. Serializable
  8. AnyRef
  9. Any
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Visibility
  1. Public
  2. All

Instance Constructors

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

    weights

    Weights computed for every feature.

    intercept

    Intercept computed for this model.

    Annotations
    @Since( "1.1.0" )

Value Members

  1. def clearThreshold(): SVMModel.this.type

    Clears the threshold so that predict will output raw prediction scores.

    Clears the threshold so that predict will output raw prediction scores.

    Annotations
    @Since( "1.0.0" )
  2. def getThreshold: Option[Double]

    Returns the threshold (if any) used for converting raw prediction scores into 0/1 predictions.

    Returns the threshold (if any) used for converting raw prediction scores into 0/1 predictions.

    Annotations
    @Since( "1.3.0" )
  3. val intercept: Double
    Definition Classes
    SVMModelGeneralizedLinearModel
    Annotations
    @Since( "0.8.0" )
  4. 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
    ClassificationModel
    Annotations
    @Since( "1.0.0" )
  5. 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" )
  6. 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" )
  7. 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
    SVMModelSaveable
    Annotations
    @Since( "1.3.0" )
  8. def setThreshold(threshold: Double): SVMModel.this.type

    Sets the threshold that separates positive predictions from negative predictions.

    Sets the threshold that separates positive predictions from negative predictions. An example with prediction score greater than or equal to this threshold is identified as a positive, and negative otherwise. The default value is 0.0.

    Annotations
    @Since( "1.0.0" )
  9. 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" )
  10. 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" )
  11. 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" )
  12. 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" )
  13. def toString(): String

    Print a summary of the model.

    Print a summary of the model.

    Definition Classes
    SVMModelGeneralizedLinearModel → AnyRef → Any
  14. val weights: Vector
    Definition Classes
    SVMModelGeneralizedLinearModel
    Annotations
    @Since( "1.0.0" )