public class BinaryLogisticRegressionTrainingSummaryImpl extends BinaryLogisticRegressionSummaryImpl implements BinaryLogisticRegressionTrainingSummary
 param:  predictions dataframe output by the model's transform method.
 param:  probabilityCol field in "predictions" which gives the probability of
                       each class as a vector.
 param:  predictionCol field in "predictions" which gives the prediction for a data instance as a
                      double.
 param:  labelCol field in "predictions" which gives the true label of each instance.
 param:  featuresCol field in "predictions" which gives the features of each instance as a vector.
 param:  weightCol field in "predictions" which gives the weight of each instance.
 param:  objectiveHistory objective function (scaled loss + regularization) at each iteration.
| Constructor and Description | 
|---|
| BinaryLogisticRegressionTrainingSummaryImpl(Dataset<Row> predictions,
                                           String probabilityCol,
                                           String predictionCol,
                                           String labelCol,
                                           String featuresCol,
                                           String weightCol,
                                           double[] objectiveHistory) | 
| Modifier and Type | Method and Description | 
|---|---|
| double[] | objectiveHistory()objective function (scaled loss + regularization) at each iteration. | 
areaUnderROC, fMeasureByThreshold, pr, precisionByThreshold, recallByThreshold, rocfeaturesCol, labelCol, predictionCol, predictions, probabilityCol, weightColequals, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitscoreColareaUnderROC, fMeasureByThreshold, pr, precisionByThreshold, recallByThreshold, rocasBinary, featuresCol, probabilityColaccuracy, falsePositiveRateByLabel, fMeasureByLabel, fMeasureByLabel, labelCol, labels, precisionByLabel, predictionCol, predictions, recallByLabel, truePositiveRateByLabel, weightCol, weightedFalsePositiveRate, weightedFMeasure, weightedFMeasure, weightedPrecision, weightedRecall, weightedTruePositiveRatetotalIterationspublic double[] objectiveHistory()
TrainingSummaryobjectiveHistory in interface TrainingSummary