org.apache.spark.mllib.classification

Class LogisticRegressionModel

Object

org.apache.spark.mllib.regression.GeneralizedLinearModel

org.apache.spark.mllib.classification.LogisticRegressionModel

All Implemented Interfaces:

java.io.Serializable, ClassificationModel

public class LogisticRegressionModel
extends GeneralizedLinearModel
implements ClassificationModel, scala.Serializable

Classification model trained using Logistic Regression.

See Also:

Serialized Form

Method Summary

Methods

Modifier and Type	Method and Description
LogisticRegressionModel	clearThreshold() :: Experimental :: Clears the threshold so that predict will output raw prediction scores.
double	intercept()
LogisticRegressionModel	setThreshold(double threshold) :: Experimental :: Sets the threshold that separates positive predictions from negative predictions.
Vector	weights()

Methods inherited from class org.apache.spark.mllib.regression.GeneralizedLinearModel

predict, predict

Methods inherited from class Object

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

Methods inherited from interface org.apache.spark.mllib.classification.ClassificationModel

predict, predict, predict

Method Detail

weights

public Vector weights()

Overrides:

weights in class GeneralizedLinearModel

intercept

public double intercept()

Overrides:

intercept in class GeneralizedLinearModel

setThreshold

public LogisticRegressionModel setThreshold(double threshold)

:: Experimental :: 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 an positive, and negative otherwise. The default value is 0.5.

clearThreshold

public LogisticRegressionModel clearThreshold()

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