Packages

c

org.apache.spark.mllib.regression

GeneralizedLinearModel

abstract class GeneralizedLinearModel extends Serializable

GeneralizedLinearModel (GLM) represents a model trained using GeneralizedLinearAlgorithm. GLMs consist of a weight vector and an intercept.

Annotations
@Since("0.8.0")
Source
GeneralizedLinearAlgorithm.scala
Linear Supertypes
Serializable, AnyRef, Any
Known Subclasses
LogisticRegressionModel, SVMModel, LassoModel, LinearRegressionModel, RidgeRegressionModel
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Inherited
  1. GeneralizedLinearModel
  2. Serializable
  3. AnyRef
  4. Any
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Visibility
  1. Public
  2. Protected

Instance Constructors

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

    weights

    Weights computed for every feature.

    intercept

    Intercept computed for this model.

    Annotations
    @Since("1.0.0")

Concrete Value Members

  1. val intercept: Double
    Annotations
    @Since("0.8.0")
  2. 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

    Annotations
    @Since("1.0.0")
  3. 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

    Annotations
    @Since("1.0.0")
  4. def toString(): String

    Print a summary of the model.

    Print a summary of the model.

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