org.apache.spark.mllib.regression

Class GeneralizedLinearModel

Object

org.apache.spark.mllib.regression.GeneralizedLinearModel

All Implemented Interfaces:

java.io.Serializable

Direct Known Subclasses:

LassoModel, LinearRegressionModel, LogisticRegressionModel, RidgeRegressionModel, SVMModel

public abstract class GeneralizedLinearModel
extends Object
implements scala.Serializable

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

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
GeneralizedLinearModel(Vector weights, double intercept)

Method Summary

Methods

Modifier and Type	Method and Description
double	intercept()
RDD<Object>	predict(RDD<Vector> testData) Predict values for the given data set using the model trained.
double	predict(Vector testData) Predict values for a single data point using the model trained.
Vector	weights()

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

GeneralizedLinearModel

public GeneralizedLinearModel(Vector weights,
                      double intercept)

Method Detail

weights

public Vector weights()

intercept

public double intercept()

predict

public RDD<Object> predict(RDD<Vector> testData)

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

Parameters:

testData - RDD representing data points to be predicted

Returns:

RDD[Double] where each entry contains the corresponding prediction

predict

public double predict(Vector testData)

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

Parameters:

testData - array representing a single data point

Returns:

Double prediction from the trained model