org.apache.spark.ml.classification

Class BinaryLogisticRegressionTrainingSummary

java.lang.Object

org.apache.spark.ml.classification.BinaryLogisticRegressionSummary

org.apache.spark.ml.classification.BinaryLogisticRegressionTrainingSummary

All Implemented Interfaces:

java.io.Serializable, LogisticRegressionSummary, LogisticRegressionTrainingSummary

public class BinaryLogisticRegressionTrainingSummary
extends BinaryLogisticRegressionSummary
implements LogisticRegressionTrainingSummary

:: Experimental :: Logistic regression training results. param: predictions dataframe outputted by the model's transform method. param: probabilityCol field in "predictions" which gives the calibrated probability of each sample as a vector. param: labelCol field in "predictions" which gives the true label of each sample. param: objectiveHistory objective function (scaled loss + regularization) at each iteration.

See Also:

Serialized Form

Method Summary

Methods

Modifier and Type	Method and Description
double[]	objectiveHistory() objective function (scaled loss + regularization) at each iteration.

Methods inherited from class org.apache.spark.ml.classification.BinaryLogisticRegressionSummary

areaUnderROC, fMeasureByThreshold, labelCol, pr, precisionByThreshold, predictions, probabilityCol, recallByThreshold, roc

Methods inherited from class java.lang.Object

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

Methods inherited from interface org.apache.spark.ml.classification.LogisticRegressionTrainingSummary

totalIterations

Methods inherited from interface org.apache.spark.ml.classification.LogisticRegressionSummary

labelCol, predictions, probabilityCol

Method Detail

objectiveHistory

public double[] objectiveHistory()

Description copied from interface: LogisticRegressionTrainingSummary

objective function (scaled loss + regularization) at each iteration.

Specified by:

objectiveHistory in interface LogisticRegressionTrainingSummary