org.apache.spark.ml.classification

Interface LogisticRegressionTrainingSummary

All Superinterfaces:

LogisticRegressionSummary, java.io.Serializable

All Known Subinterfaces:

BinaryLogisticRegressionTrainingSummary

All Known Implementing Classes:

BinaryLogisticRegressionTrainingSummaryImpl, LogisticRegressionTrainingSummaryImpl

public interface LogisticRegressionTrainingSummary
extends LogisticRegressionSummary

:: Experimental :: Abstraction for multiclass logistic regression training results. Currently, the training summary ignores the training weights except for the objective trace.

Method Summary

Modifier and Type	Method and Description
double[]	objectiveHistory() objective function (scaled loss + regularization) at each iteration.
int	totalIterations() Number of training iterations.

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

accuracy, asBinary, falsePositiveRateByLabel, featuresCol, fMeasureByLabel, fMeasureByLabel, labelCol, labels, multiclassMetrics, precisionByLabel, predictionCol, predictions, probabilityCol, recallByLabel, truePositiveRateByLabel, weightedFalsePositiveRate, weightedFMeasure, weightedFMeasure, weightedPrecision, weightedRecall, weightedTruePositiveRate

Method Detail

objectiveHistory

double[] objectiveHistory()

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

totalIterations

int totalIterations()

Number of training iterations.